:Search:

Machine Learning Specialization

Torrent:
Info Hash: 1C6FE20E31AFAB2F041D8BC4299B968B95EEE660
Thumbnail:
Similar Posts:
More Other:
Uploader: tutsnode
Source: T Logo Torrent Galaxy
Downloads: 4320
Language: English
Description:
None
Category: Other
Size: 13.2 GB
Added: July 18, 2023, 7:02 p.m.
Peers: Seeders: 13, Leechers: 1 (Last updated: 2 days, 7 hours ago)
Tracker Data:
Tracker Seeders Leechers Completed
udp://open.stealth.si:80/announce 8 4 3568
udp://exodus.desync.com:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.cyberia.is:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.opentrackr.org:1337/announce 9 4 587
udp://tracker.torrent.eu.org:451/announce 8 4 132
udp://explodie.org:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.birkenwald.de:6969/announce 0 1 0
udp://tracker.moeking.me:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://ipv4.tracker.harry.lu:80/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.tiny-vps.com:6969/announce 1 1 33
udp://tracker.therarbg.to:6969/announce 0 1 0
Files:
  1. 01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4 236.2 MB
  2. TutsNode.net.txt 63 bytes
  3. 01_slides-presented-in-this-module_LM-3dtexton.pdf 1.8 MB
  4. 01_welcome.en.txt 2.8 KB
  5. 01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.argsort.html 38.8 KB
  6. 02_assessing-performance-intro.en.txt 464 bytes
  7. 0 16 bytes
  8. 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 230.7 MB
  9. 07_optional-a-worked-out-example-for-kd-trees_instructions.html 1.3 MB
  10. 01_predicting-sentiment-from-product-reviews_sklearn.linear_model.LogisticRegression.html 82.6 KB
  11. 1 9 bytes
  12. 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4 214.7 MB
  13. 02_deep-learning_exam.html 1.3 MB
  14. 01_clustering-text-data-with-gaussian-mixtures_instructions.html 21.3 KB
  15. 2 12 bytes
  16. 01_recommending-songs-assignment_song_data.csv 149.2 MB
  17. 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip 834.3 KB
  18. 01_implementing-binary-decision-trees_instructions.html 30.7 KB
  19. 3 2 bytes
  20. 01_retrieving-wikipedia-articles-assignment_people_wiki.csv 110.9 MB
  21. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_home_data.sframe.zip 908.2 KB
  22. 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html 243.2 KB
  23. 02_formally-deriving-why-3-sources-of-error.en.srt 21.8 KB
  24. 06_a-brief-recap.en.srt 1.6 KB
  25. 4 8 bytes
  26. 01_deep-features-for-image-retrieval-assignment_image_test_data.csv 103.0 MB
  27. 01_predicting-house-prices-assignment_home_data.sframe.zip 908.2 KB
  28. 02_clustering-and-similarity_exam.html 109.2 KB
  29. 03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt 7.0 KB
  30. 5 2 bytes
  31. 01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip 87.7 MB
  32. 01_implementing-locality-sensitive-hashing-from-scratch_instructions.html 328.0 KB
  33. 01_what-is-machine-learning.en.srt 9.5 KB
  34. 6 12 bytes
  35. 01_analyzing-product-sentiment-assignment_amazon_baby.csv 85.3 MB
  36. 01_slides-presented-in-this-module_eccv06.pdf 706.4 KB
  37. 04_ethical-use-of-recommender-systems.en.txt 9.0 KB
  38. 7 1 byte
  39. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.sframe.zip 56.2 MB
  40. 02_searching-for-images-a-case-study-in-deep-learning.mp4 1.7 MB
  41. 01_exploring-ensemble-methods_sklearn.ensemble.GradientBoostingClassifier.html 83.4 KB
  42. 01_slides-presented-in-this-module_instructions.html 3.1 KB
  43. 8 7 bytes
  44. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.sframe.zip 56.2 MB
  45. 01_slides-presented-in-this-module_decision-trees-missing-values-annotated.pdf 1.6 MB
  46. 03_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip 198.4 KB
  47. 01_recap-of-overfitting-regularization-in-logistic-regression.en.srt 1.3 KB
  48. 9 2 bytes
  49. 01_clustering-text-data-with-k-means_people_wiki.sframe.zip 56.2 MB
  50. 01_slides-presented-in-this-module_imagenet.pdf 1.3 MB
  51. 01_predicting-house-prices-using-k-nearest-neighbors-regression_home_data_small.sframe.zip 386.9 KB
  52. 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt 31.7 KB
  53. 02_k-nearest-neighbors-in-practice.en.txt 3.2 KB
  54. 10 3 bytes
  55. 01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip 56.2 MB
  56. 02_multiple-regression-intro.mp4 1.2 MB
  57. 01_slides-presented-in-this-module_iccv99.pdf 564.4 KB
  58. 01_getting-started-with-python-jupyter-notebook-turi-create_Turi_Getting_Started_with_SFrames.ipynb.zip 1.7 KB
  59. 11 7 bytes
  60. 01_modeling-text-topics-with-latent-dirichlet-allocation_people_wiki.sframe.zip 56.2 MB
  61. 01_logistic-regression-with-l2-regularization_module-4-assignment-numpy-arrays.npz.zip 1.2 MB
  62. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_valid_shuffled.csv.zip 610.0 KB
  63. 04_a-brief-recap.en.txt 1.4 KB
  64. 12 10 bytes
  65. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.sframe.zip 56.2 MB
  66. 01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-numpy-arrays.npz.zip 1.2 MB
  67. 01_slides-presented-in-this-module_Dalal-cvpr05.pdf 445.4 KB
  68. 06_reading-software-tools-you-ll-need_quickstart.html 166.3 KB
  69. 05_impact-of-classification.en.srt 1.4 KB
  70. 01_multiclass.en.txt 2.8 KB
  71. 01_slides-presented-in-this-module_precision-recall.pdf 1.9 MB
  72. 01_practice-quiz-multiple-linear-regression_exam.html 69.8 KB
  73. 13 67 bytes
  74. 01_download-wiki-people-data_people_wiki.sframe.zip 56.2 MB
  75. 01_implementing-logistic-regression-from-scratch_module-3-assignment-numpy-arrays.npz.zip 1.2 MB
  76. 01_polynomial-regression_wk3_kc_house_train_data.csv.zip 354.7 KB
  77. 01_deep-features-for-image-retrieval-assignment_tabular-data.html 243.2 KB
  78. 03_collaborative-filtering-algorithm.en.srt 18.8 KB
  79. 14 48 bytes
  80. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people_wiki.sframe.zip 56.2 MB
  81. 01_fitting-a-simple-linear-regression-model-on-housing-data_home_data.sframe.zip 908.2 KB
  82. 02_recommender-systems_exam.html 789.1 KB
  83. 01_boosting-a-decision-stump_instructions.html 103.4 KB
  84. 04_using-probabilities-in-classification.en.txt 2.1 KB
  85. 15 2 bytes
  86. 01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip 56.2 MB
  87. 01_exploring-different-multiple-regression-models-for-house-price-prediction_home_data.sframe.zip 908.2 KB
  88. 01_implementing-em-for-gaussian-mixtures_instructions.html 786.6 KB
  89. 01_practice-quiz-gradient-descent-for-logistic-regression_exam.html 102.1 KB
  90. 03_test-error-what-we-can-actually-compute.en.srt 5.9 KB
  91. 16 2 bytes
  92. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.gl.zip 55.6 MB
  93. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_train_data.csv.zip 354.7 KB
  94. 01_polynomial-regression_wk3_kc_house_test_data.csv.zip 81.7 KB
  95. 02_multiclass-classification-with-decision-trees.en.srt 3.6 KB
  96. 17 3 bytes
  97. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip 55.6 MB
  98. 01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip 354.7 KB
  99. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_test_data.csv.zip 81.7 KB
  100. 01_recommender-systems-ml-block-diagram.en.txt 3.6 KB
  101. 18 10 bytes
  102. 01_clustering-text-data-with-k-means_people_wiki.gl.zip 55.6 MB
  103. 01_implementing-em-for-gaussian-mixtures_chosen_images.png 352.9 KB
  104. 01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip 81.7 KB
  105. 02_outline-of-first-half-of-course.en.txt 5.4 KB
  106. 19 9 bytes
  107. 01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip 55.6 MB
  108. 01_deep-features-for-image-retrieval-assignment_instructions.html 350.4 KB
  109. 01_clustering-text-data-with-k-means_instructions.html 78.8 KB
  110. 02_motiving-probabilistic-clustering-models.en.srt 10.7 KB
  111. 20 38 bytes
  112. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.gl.zip 55.6 MB
  113. 01_polynomial-regression_wk3_kc_house_valid_data.csv.zip 350.0 KB
  114. 01_practice-quiz-the-problem-of-overfitting_exam.html 89.8 KB
  115. 01_getting-started-with-python-jupyter-notebook-turi-create_people-example.csv 248 bytes
  116. 21 48 bytes
  117. 07_optional-improving-efficiency-through-multiple-tables.mp4 54.2 MB
  118. 02_assessing-performance-intro.mp4 1.8 MB
  119. 01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html 28.2 KB
  120. 02_you-ve-made-it.en.srt 1.0 KB
  121. 22 12 bytes
  122. 01_deep-features-for-image-retrieval-assignment_image_train_data.csv 51.6 MB
  123. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_valid_data.csv.zip 350.0 KB
  124. 01_practice-quiz-cost-function-for-logistic-regression_exam.html 77.0 KB
  125. 03_using-the-fitted-line.en.txt 3.9 KB
  126. 23 4 bytes
  127. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_tf_idf.npz.zip 50.9 MB
  128. 02_implementing-gradient-descent-for-multiple-regression_home_data.sframe.zip 908.2 KB
  129. 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md 176.1 KB
  130. 01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html 18.2 KB
  131. 24 85 bytes
  132. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_tf_idf.npz.zip 50.9 MB
  133. 01_polynomial-regression_home_data.sframe.zip 908.2 KB
  134. 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_train.csv.zip 171.7 KB
  135. 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.metrics.pairwise.euclidean_distances.html 21.7 KB
  136. 02_a-case-study-in-predicting-house-prices.en.txt 934 bytes
  137. 25 52 bytes
  138. 01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip 50.9 MB
  139. 01_observing-effects-of-l2-penalty-in-polynomial-regression_home_data.sframe.zip 908.2 KB
  140. 07_software-tools-you-ll-need-for-this-course_quickstart.html 166.3 KB
  141. 03_demand-prediction.en.srt 26.6 KB
  142. 02_thank-you.en.txt 1.4 KB
  143. 26 22 bytes
  144. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_tf_idf.npz.zip 50.9 MB
  145. 01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip 908.2 KB
  146. 08_reading-software-tools-you-ll-need_quickstart.html 166.3 KB
  147. 02_computation-graph-optional.en.srt 26.4 KB
  148. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 1.6 KB
  149. 27 4 bytes
  150. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip 50.9 MB
  151. 01_using-lasso-to-select-features_home_data.sframe.zip 908.2 KB
  152. 04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip 198.4 KB
  153. 03_what-you-ll-be-able-to-do.en.txt 918 bytes
  154. 28 1 byte
  155. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_test_data.zip 50.9 MB
  156. 01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip 908.2 KB
  157. 04_download-the-notebook-and-follow-along_Overfitting_Demo_Ridge_Lasso.ipynb.zip 198.4 KB
  158. 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt 886 bytes
  159. 29 33 bytes
  160. 01_deep-features-for-image-retrieval-assignment_image_test_data.zip 50.9 MB
  161. 01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_data.csv.zip 780.7 KB
  162. 01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html 297.9 KB
  163. 07_optional-improving-efficiency-through-multiple-tables.en.srt 26.0 KB
  164. 06_effect-of-coefficient-values-on-decision-boundary.en.srt 3.0 KB
  165. 30 14 bytes
  166. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_song_data.sframe.zip 48.0 MB
  167. 01_exploring-ensemble-methods_instructions.html 31.2 KB
  168. 03_computing-coefficient-of-each-ensemble-component.en.txt 3.7 KB
  169. 31 1 byte
  170. 01_recommending-songs-assignment_song_data.sframe.zip 48.0 MB
  171. 01_implementing-logistic-regression-from-scratch_instructions.html 30.8 KB
  172. 04_defining-more-bins.en.srt 4.2 KB
  173. 32 7 bytes
  174. 01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip 47.6 MB
  175. 01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip 350.0 KB
  176. 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt 39.9 KB
  177. 04_linear-regression-a-model-based-approach.en.txt 4.1 KB
  178. 33 12 bytes
  179. 02_formally-deriving-why-3-sources-of-error.mp4 46.7 MB
  180. 01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_data.csv.zip 780.7 KB
  181. 01_exploring-ensemble-methods_boosted_trees_classifier.html 283.6 KB
  182. 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small.csv.zip 269.2 KB
  183. 03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt 9.5 KB
  184. 34 4 bytes
  185. 01_formally-defining-the-3-sources-of-error.mp4 45.2 MB
  186. 02_where-should-my-files-go_instructions.html 757.3 KB
  187. 01_reducing-the-number-of-features-optional.en.srt 18.1 KB
  188. 35 15 bytes
  189. 01_deriving-the-lasso-coordinate-descent-update.mp4 42.7 MB
  190. 02_implementing-gradient-descent-for-multiple-regression_kc_house_data.csv.zip 780.7 KB
  191. [TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
  192. 01_exploring-ensemble-methods_graphlab.SFrame.to_numpy.html 283.6 KB
  193. 04_optional-a-worked-out-example-for-em_instructions.html 210.8 KB
  194. 01_welcome-to-machine-learning.en.txt 2.4 KB
  195. 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.CountVectorizer.html 57.5 KB
  196. 03_welcome-to-this-course-and-specialization.en.txt 637 bytes
  197. 36 3 bytes
  198. 01_predicting-sentiment-from-product-reviews_amazon_baby.gl.zip 40.3 MB
  199. 01_polynomial-regression_kc_house_data.csv.zip 780.7 KB
  200. 01_observing-effects-of-l2-penalty-in-polynomial-regression_kc_house_data.csv.zip 780.7 KB
  201. 01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html 131.9 KB
  202. 01_mean-normalization.en.srt 10.7 KB
  203. 37 15 bytes
  204. 01_exploring-precision-and-recall_amazon_baby.gl.zip 40.3 MB
  205. 01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip 780.7 KB
  206. 01_using-lasso-to-select-features_kc_house_data.csv.zip 780.7 KB
  207. 01_training-logistic-regression-via-stochastic-gradient-ascent_instructions.html 126.4 KB
  208. 07_optional-improving-efficiency-through-multiple-tables.en.txt 15.9 KB
  209. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_people-example.csv 248 bytes
  210. 38 11 bytes
  211. 01_predicting-sentiment-from-product-reviews_amazon_baby.sframe.zip 40.3 MB
  212. 01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip 780.7 KB
  213. 02_regression_exam.html 676.9 KB
  214. 02_deep-features-for-image-retrieval_exam.html 165.1 KB
  215. 01_identifying-safe-loans-with-decision-trees_sklearn.tree.DecisionTreeClassifier.html 74.9 KB
  216. 03_a-standard-gibbs-sampler-for-lda.en.srt 11.4 KB
  217. 01_practice-quiz-supervised-vs-unsupervised-learning_exam.html 1.9 KB
  218. 01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_train_data.csv.zip 628.5 KB
  219. 01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_train_data.csv.zip 628.5 KB
  220. 02_implementing-gradient-descent-for-multiple-regression_kc_house_train_data.csv.zip 628.5 KB
  221. 01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip 158.7 KB
  222. 05_querying-for-the-most-similar-images-for-car-image.en.srt 2.1 KB
  223. 39 2 bytes
  224. 01_exploring-precision-and-recall_amazon_baby.sframe.zip 40.3 MB
  225. 01_implementing-ridge-regression-via-gradient-descent_kc_house_train_data.csv.zip 628.5 KB
  226. 01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip 628.5 KB
  227. 01_predicting-house-prices-assignment_graphlab.SFrame.html 281.4 KB
  228. 01_practice-quiz-gradient-descent-in-practice_exam.html 164.9 KB
  229. 02_the-boosting-question.en.srt 5.9 KB
  230. 40 3 bytes
  231. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_amazon_baby.sframe.zip 40.3 MB
  232. 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip 623.6 KB
  233. 01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html 281.4 KB
  234. 01_choosing-features-and-metrics-for-nearest-neighbor-search_graphlab.SFrame.join.html 281.4 KB
  235. 01_recommending-songs-assignment_graphlab.SFrame.groupby.html 281.4 KB
  236. 01_practice-quiz-regression_exam.html 2.6 KB
  237. 01_predicting-sentiment-from-product-reviews_module-2-assignment-train-idx.json.zip 163.9 KB
  238. 01_exploring-precision-and-recall_instructions.html 70.9 KB
  239. 01_computing-the-gradient-of-rss.en.srt 3.7 KB
  240. 41 1 byte
  241. 01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip 40.3 MB
  242. 01_exploring-precision-and-recall_module-9-assignment-train-idx.json.zip 163.9 KB
  243. 01_numpy-tutorial_quickstart.html 162.9 KB
  244. 01_implementing-locality-sensitive-hashing-from-scratch_itertools.html 161.2 KB
  245. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_3_data.csv.zip 158.7 KB
  246. 01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip 158.6 KB
  247. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_1_data.csv.zip 158.6 KB
  248. 01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip 158.3 KB
  249. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_4_data.csv.zip 158.3 KB
  250. 01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip 157.6 KB
  251. 01_observing-effects-of-l2-penalty-in-polynomial-regression_wk3_kc_house_set_2_data.csv.zip 157.6 KB
  252. 01_analyzing-product-sentiment-assignment_datastructures.html 93.1 KB
  253. 01_predicting-house-prices-using-k-nearest-neighbors-regression_instructions.html 19.9 KB
  254. 42 107 bytes
  255. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki.csv.zip 39.9 MB
  256. 01_choosing-features-and-metrics-for-nearest-neighbor-search_instructions.html 123.6 KB
  257. 01_what-is-a-derivative-optional.en.txt 15.2 KB
  258. 43 178 bytes
  259. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip 39.9 MB
  260. 01_logistic-regression-with-l2-regularization_instructions.html 90.5 KB
  261. 01_logistic-regression-with-l2-regularization_module-4-assignment-train-idx.json.zip 47.1 KB
  262. 03_creating-the-word-count-vector.en.txt 1.3 KB
  263. 44 16 bytes
  264. 01_clustering-text-data-with-k-means_people_wiki.csv.zip 39.9 MB
  265. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND02-NB01.ipynb.zip 67.3 KB
  266. 01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip 67.3 KB
  267. 02_retrieval-as-k-nearest-neighbor-search.en.srt 4.4 KB
  268. 45 4 bytes
  269. 01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip 39.9 MB
  270. 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt 63.9 KB
  271. 01__MentorProgramInformation.pdf 62.7 KB
  272. 01_gradient-descent.en.srt 12.2 KB
  273. 46 2 bytes
  274. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.csv.zip 39.9 MB
  275. 01_choosing-features-and-metrics-for-nearest-neighbor-search_sklearn.feature_extraction.text.TfidfVectorizer.html 61.6 KB
  276. 01_implementing-locality-sensitive-hashing-from-scratch_sklearn.feature_extraction.text.TfidfVectorizer.html 61.6 KB
  277. 03_the-return-in-reinforcement-learning.en.srt 15.7 KB
  278. 47 79 bytes
  279. 01_what-is-a-derivative-optional.mp4 38.3 MB
  280. 01_fitting-a-simple-linear-regression-model-on-housing-data_kc_house_test_data.csv.zip 154.8 KB
  281. 01_exploring-different-multiple-regression-models-for-house-price-prediction_kc_house_test_data.csv.zip 154.8 KB
  282. 05_feature-engineering.en.txt 2.5 KB
  283. 02_implementing-gradient-descent-for-multiple-regression_kc_house_test_data.csv.zip 154.8 KB
  284. 01_implementing-ridge-regression-via-gradient-descent_kc_house_test_data.csv.zip 154.8 KB
  285. 01_implementing-lasso-using-coordinate-descent_kc_house_test_data.csv.zip 154.8 KB
  286. 01_clustering-text-data-with-k-means_sklearn.feature_extraction.text.TfidfVectorizer.html 61.6 KB
  287. 01_clustering-text-data-with-gaussian-mixtures_sklearn.feature_extraction.text.TfidfVectorizer.html 61.6 KB
  288. 01_week-2-practice-lab-linear-regression_instructions.html 2.6 KB
  289. 01_modeling-text-data-with-a-hierarchy-of-clusters_sklearn.feature_extraction.text.TfidfVectorizer.html 61.6 KB
  290. 01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html 60.8 KB
  291. 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.srt 59.7 KB
  292. 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_test.csv.zip 55.2 KB
  293. 01_identifying-safe-loans-with-decision-trees_module-5-assignment-1-train-idx.json.zip 55.1 KB
  294. 01_implementing-binary-decision-trees_module-5-assignment-2-train-idx.json.zip 55.1 KB
  295. 01_boosting-a-decision-stump_module-8-assignment-2-train-idx.json.zip 55.1 KB
  296. 01_decision-trees-in-practice_module-6-assignment-train-idx.json.zip 55.1 KB
  297. 01_exploring-ensemble-methods_module-8-assignment-1-train-idx.json.zip 55.0 KB
  298. 01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-train-idx.json.zip 53.1 KB
  299. 01_practice-quiz-classification-with-logistic-regression_exam.html 51.5 KB
  300. 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt 50.6 KB
  301. 01_predicting-sentiment-from-product-reviews_module-2-assignment-test-idx.json.zip 46.6 KB
  302. 01_exploring-precision-and-recall_module-9-assignment-test-idx.json.zip 46.6 KB
  303. 01_predicting-house-prices-using-k-nearest-neighbors-regression_kc_house_data_small_validation.csv.zip 45.7 KB
  304. 02_classification_exam.html 35.6 KB
  305. 48 133 bytes
  306. 02_where-is-ml-going.mp4 37.8 MB
  307. 01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip 44.9 KB
  308. 01_decision-trees-in-practice_instructions.html 38.7 KB
  309. 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.txt 30.7 KB
  310. 01_what-is-a-derivative-optional.en.srt 29.7 KB
  311. 05_optional-reading-worked-out-example-for-gradient-descent_instructions.html 29.5 KB
  312. 01_predicting-house-prices-assignment_instructions.html 29.4 KB
  313. 05_prioritizing-important-words-with-tf-idf.en.srt 5.0 KB
  314. 49 1 byte
  315. 02_tensorflow-implementation-of-collaborative-filtering.mp4 35.9 MB
  316. 03_learning-the-state-value-function.en.srt 25.2 KB
  317. 01_identifying-safe-loans-with-decision-trees_instructions.html 25.0 KB
  318. 01_predicting-sentiment-from-product-reviews_instructions.html 24.9 KB
  319. 02_pca-algorithm-optional.en.srt 24.4 KB
  320. 01_practice-quiz-train-the-model-with-gradient-descent_exam.html 21.4 KB
  321. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND05-NB01.ipynb.zip 15.1 KB
  322. 50 147 bytes
  323. 02_applications-of-machine-learning.mp4 33.4 MB
  324. 03_model-selection-and-training-cross-validation-test-sets.en.srt 21.3 KB
  325. 02_training-details.en.srt 20.9 KB
  326. 01_week-3-practice-lab-logistic-regression_instructions.html 1.1 KB
  327. 01_modeling-text-data-with-a-hierarchy-of-clusters_instructions.html 20.5 KB
  328. 01_deriving-the-lasso-coordinate-descent-update.en.srt 20.2 KB
  329. 04_cost-function-intuition.en.srt 20.2 KB
  330. 04_transfer-learning-using-data-from-a-different-task.en.srt 20.2 KB
  331. 01_important-update-regarding-the-machine-learning-specialization_instructions.html 1.8 KB
  332. 02_slides-presented-in-this-module_instructions.html 1.1 KB
  333. 04_learning-curves.en.srt 20.1 KB
  334. 03_welcome-to-the-classification-course-a-part-of-the-machine-learning.en.srt 1.7 KB
  335. 03_welcome-to-the-classification-course-a-part-of-the-machine-learning.en.txt 1.1 KB
  336. 01_observing-effects-of-l2-penalty-in-polynomial-regression_instructions.html 19.8 KB
  337. 01_implementing-lasso-using-coordinate-descent_instructions.html 19.6 KB
  338. 01_formally-defining-the-3-sources-of-error.en.srt 19.3 KB
  339. 03_adding-data.en.srt 18.9 KB
  340. 01_identifying-safe-loans-with-decision-trees_module-5-assignment-1-validation-idx.json.zip 18.9 KB
  341. 05_impact-of-classification.en.txt 933 bytes
  342. 01_decision-trees-in-practice_module-6-assignment-validation-idx.json.zip 18.9 KB
  343. 01_exploring-ensemble-methods_module-8-assignment-1-validation-idx.json.zip 18.9 KB
  344. 01_implementing-binary-decision-trees_module-5-assignment-2-test-idx.json.zip 18.9 KB
  345. 01_boosting-a-decision-stump_module-8-assignment-2-test-idx.json.zip 18.9 KB
  346. 06_choosing-what-features-to-use.en.srt 18.7 KB
  347. 01_implementing-ridge-regression-via-gradient-descent_instructions.html 18.6 KB
  348. 01_the-problem-of-overfitting.en.srt 18.3 KB
  349. 02_trading-off-precision-and-recall.en.srt 18.3 KB
  350. 02_neurons-and-the-brain.en.srt 18.3 KB
  351. 02_learning-process.en.srt 18.0 KB
  352. 01_diagnosing-bias-and-variance.en.srt 17.8 KB
  353. 03_bellman-equation.en.srt 17.7 KB
  354. 06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt 17.7 KB
  355. 05_lets-get-started.en.srt 1.0 KB
  356. 05_lets-get-started.en.txt 567 bytes
  357. 04_developing-and-evaluating-an-anomaly-detection-system.en.srt 17.5 KB
  358. 02_choosing-a-split-information-gain.en.srt 17.4 KB
  359. 01_analyzing-product-sentiment-assignment_instructions.html 17.3 KB
  360. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  361. 01_cost-function-for-logistic-regression.en.srt 17.1 KB
  362. 02_implementing-gradient-descent.en.txt 7.5 KB
  363. 02_linear-classifiers-a-motivating-example.en.txt 2.4 KB
  364. 01_error-metrics-for-skewed-datasets.en.srt 17.0 KB
  365. 01_logistic-regression-with-l2-regularization_module-4-assignment-validation-idx.json.zip 16.9 KB
  366. 04_ethical-use-of-recommender-systems.en.srt 16.9 KB
  367. 03_pca-in-code-optional.en.srt 16.7 KB
  368. 01_is-there-a-path-to-agi.en.srt 16.4 KB
  369. 04_decision-boundaries.en.txt 2.8 KB
  370. 02_regularization-and-bias-variance.en.srt 16.3 KB
  371. 03_establishing-a-baseline-level-of-performance.en.srt 15.8 KB
  372. 02_module-1-recap.en.srt 15.6 KB
  373. 02_tensorflow-implementation-of-collaborative-filtering.en.srt 15.4 KB
  374. 02_softmax.en.srt 15.4 KB
  375. 06_effect-of-coefficient-values-on-decision-boundary.en.txt 1.8 KB
  376. 01_recommending-songs-assignment_FND05-NB01.ipynb.zip 15.1 KB
  377. 07_using-features-of-the-inputs.en.srt 2.9 KB
  378. 07_using-features-of-the-inputs.en.txt 1.8 KB
  379. 02_gaussian-normal-distribution.en.srt 15.0 KB
  380. 01_predicting-class-probabilities.en.srt 2.3 KB
  381. 01_predicting-class-probabilities.en.txt 1.4 KB
  382. 02_implementing-gradient-descent-for-multiple-regression_instructions.html 14.9 KB
  383. 01_linear-regression-model-part-1.en.srt 14.8 KB
  384. 03_putting-it-together.en.srt 14.6 KB
  385. 06_bias-variance-and-neural-networks.en.srt 14.6 KB
  386. 01_finding-unusual-events.en.srt 14.5 KB
  387. 05_full-cycle-of-a-machine-learning-project.en.srt 14.5 KB
  388. 05_deciding-what-to-try-next-revisited.en.srt 14.5 KB
  389. 01_polynomial-regression_instructions.html 14.5 KB
  390. 01_collaborative-filtering-vs-content-based-filtering.en.srt 14.3 KB
  391. 03_k-means-algorithm.en.srt 14.2 KB
  392. 05_algorithm-refinement-greedy-policy.en.srt 14.2 KB
  393. 02_implementing-gradient-descent.en.srt 14.2 KB
  394. 02_deep-learning-for-content-based-filtering.en.srt 14.2 KB
  395. 03_decision-boundary.en.srt 14.1 KB
  396. 03_demand-prediction.en.txt 14.0 KB
  397. 01_clustering-text-data-with-k-means_CLU03-NB01.ipynb.zip 14.0 KB
  398. 02_choosing-activation-functions.en.srt 13.8 KB
  399. 03_logistic-regression-model.en.txt 2.8 KB
  400. 01_using-lasso-to-select-features_instructions.html 13.8 KB
  401. 04_optimization-objective.en.srt 13.7 KB
  402. 02_computation-graph-optional.en.txt 13.6 KB
  403. 06_hidden-markov-models.en.srt 13.6 KB
  404. 04_learning-very-non-linear-features-with-neural-networks.en.srt 13.6 KB
  405. 05_overview-of-learning-logistic-regression-models.en.txt 2.0 KB
  406. 07_software-tools-you-ll-need-for-this-course_instructions.html 13.5 KB
  407. 01_state-action-value-function-definition.en.srt 13.5 KB
  408. 05_module-by-module-topics-covered.en.srt 13.5 KB
  409. 01_multiple-features.en.srt 13.4 KB
  410. 06_fairness-bias-and-ethics.en.srt 13.4 KB
  411. 04_improved-implementation-of-softmax.en.srt 13.4 KB
  412. 02_logistic-regression.en.srt 13.3 KB
  413. 01_recap-of-logistic-regression-classifier.en.srt 1.9 KB
  414. 01_recap-of-logistic-regression-classifier.en.txt 1.2 KB
  415. 03_anomaly-detection-algorithm.en.srt 13.3 KB
  416. 02_error-analysis.en.srt 13.2 KB
  417. 01_getting-started-with-python-jupyter-notebook-turi-create_instructions.html 13.2 KB
  418. 02_data-in-tensorflow.en.srt 13.2 KB
  419. 04_random-stochastic-environment-optional.en.srt 13.1 KB
  420. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND03-NB01.ipynb.zip 13.1 KB
  421. 01_analyzing-product-sentiment-assignment_FND03-NB01.ipynb.zip 13.1 KB
  422. 02_formally-deriving-why-3-sources-of-error.en.txt 13.1 KB
  423. 08_reading-software-tools-you-ll-need_instructions.html 13.0 KB
  424. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  425. 01_neural-network-layer.en.srt 13.0 KB
  426. 02_goal-learning-parameters-of-logistic-regression.en.srt 2.9 KB
  427. 02_goal-learning-parameters-of-logistic-regression.en.txt 1.9 KB
  428. 06_reading-software-tools-you-ll-need_instructions.html 12.9 KB
  429. 03_learning-the-state-value-function.en.txt 12.9 KB
  430. 01_implementing-em-for-gaussian-mixtures_scipy.stats.multivariate_normal.html 12.9 KB
  431. 02_addressing-overfitting.en.srt 12.9 KB
  432. 02_evaluating-a-model.en.srt 12.8 KB
  433. 02_pca-algorithm-optional.en.txt 12.8 KB
  434. 01_training-logistic-regression-via-stochastic-gradient-ascent_CLA10-NB01.ipynb.zip 12.7 KB
  435. 01_analyzing-product-sentiment-assignment_turicreate.SArray.apply.html 12.7 KB
  436. 05_finding-best-linear-classifier-with-gradient-ascent.en.txt 2.7 KB
  437. 01_motivations.en.srt 12.7 KB
  438. 02_using-per-item-features.en.srt 12.6 KB
  439. 01_what-is-reinforcement-learning.en.srt 12.5 KB
  440. 02_convergence-initialization-and-overfitting-of-em.en.srt 12.5 KB
  441. 03_optional-reading-worked-out-example-for-closed-form-solution_instructions.html 12.5 KB
  442. 02_learning-algorithm-for-logistic-regression.en.txt 2.6 KB
  443. 02_matrix-multiplication.en.srt 12.3 KB
  444. 05_visualizing-the-cost-function.en.srt 12.3 KB
  445. 01_clustering-text-data-with-k-means_numpy.mean.html 12.2 KB
  446. 02_analyzing-product-sentiment_exam.html 12.2 KB
  447. 06_regression-trees-optional.en.srt 12.2 KB
  448. 01_deriving-the-lasso-coordinate-descent-update.en.txt 12.2 KB
  449. 01_formally-defining-the-3-sources-of-error.en.txt 12.1 KB
  450. 05_summary-of-gradient-ascent-for-logistic-regression.en.srt 2.6 KB
  451. 05_summary-of-gradient-ascent-for-logistic-regression.en.txt 1.6 KB
  452. 01_iterative-loop-of-ml-development.en.srt 12.1 KB
  453. 01_open-challenges-in-ml.en.srt 12.0 KB
  454. 03_adding-data.en.txt 12.0 KB
  455. 04_regularized-linear-regression.en.srt 12.0 KB
  456. 05_ridge-regression-demo.en.srt 12.0 KB
  457. 06_choosing-what-features-to-use.en.txt 11.9 KB
  458. 03_larger-neural-network-example-optional.en.srt 11.9 KB
  459. 03_cost-function-formula.en.srt 11.9 KB
  460. 03_rule-of-thumb-for-choosing-step-size.en.txt 2.6 KB
  461. 02_more-complex-neural-networks.en.srt 11.7 KB
  462. 04_what-happens-after-deployment.en.srt 11.6 KB
  463. 02_general-implementation-of-forward-propagation.en.srt 11.6 KB
  464. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND04-NB01.ipynb.zip 11.6 KB
  465. 01_retrieving-wikipedia-articles-assignment_FND04-NB01.ipynb.zip 11.6 KB
  466. 02_very-optional-expressing-the-log-likelihood.en.txt 2.3 KB
  467. 04_unsupervised-learning-part-1.en.srt 11.6 KB
  468. 03_very-optional-deriving-probability-y-1-given-x.en.srt 2.1 KB
  469. 03_very-optional-deriving-probability-y-1-given-x.en.txt 1.2 KB
  470. 03_matrix-multiplication-rules.en.srt 11.4 KB
  471. 03_cost-function-with-regularization.en.srt 11.3 KB
  472. 02_additional-layer-types.en.srt 11.3 KB
  473. 03_model-selection-and-training-cross-validation-test-sets.en.txt 11.2 KB
  474. 04_learning-rate.en.srt 11.2 KB
  475. 01_implementing-em-for-gaussian-mixtures_CLU04-NB01.ipynb.zip 11.2 KB
  476. 05_anomaly-detection-vs-supervised-learning.en.srt 11.2 KB
  477. 01_recap-of-learning-logistic-regression-classifiers.en.srt 2.6 KB
  478. 01_recap-of-learning-logistic-regression-classifiers.en.txt 1.6 KB
  479. 02_where-is-ml-going.en.srt 11.2 KB
  480. 06_choosing-the-number-of-clusters.en.srt 11.2 KB
  481. 04_gradient-descent-for-multiple-linear-regression.en.srt 11.1 KB
  482. 04_assessing-the-quality-and-choosing-the-number-of-clusters.en.srt 11.0 KB
  483. 03_optional-another-perspecting-on-overfitting-in-logistic-regression.en.srt 11.0 KB
  484. 01_implementing-logistic-regression-from-scratch_important_words.json.zip 862 bytes
  485. 06_why-a-case-study-approach.en.srt 10.9 KB
  486. 02_training-details.en.txt 10.9 KB
  487. 01_decision-tree-model.en.srt 10.8 KB
  488. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  489. 01_retrieving-wikipedia-articles-assignment_turicreate.toolkits.distances.cosine.html 10.8 KB
  490. 02_feature-scaling-part-2.en.srt 10.8 KB
  491. 02_evaluating-a-classifier.en.txt 2.9 KB
  492. 02_supervised-learning-part-1.en.srt 10.8 KB
  493. 05_initializing-k-means.en.srt 10.7 KB
  494. 03_review-of-overfitting-in-regression.en.txt 3.0 KB
  495. 03_building-a-neural-network.en.srt 10.7 KB
  496. 01_recommending-songs-assignment_instructions.html 10.7 KB
  497. 04_modeling-seasonality.en.srt 10.6 KB
  498. 04_binary-labels-favs-likes-and-clicks.en.srt 10.6 KB
  499. 05_searching-neighboring-bins.en.srt 10.6 KB
  500. 01_advanced-optimization.en.srt 10.5 KB
  501. 04_transfer-learning-using-data-from-a-different-task.en.txt 10.5 KB
  502. 04_learning-curves.en.txt 10.5 KB
  503. 05_module-4-recap.en.srt 10.5 KB
  504. 05_when-to-use-decision-trees.en.srt 10.5 KB
  505. 01_optional-motivating-pruning.en.srt 10.4 KB
  506. 02_mars-rover-example.en.srt 10.3 KB
  507. 02_generalization-error-what-we-really-want.en.srt 10.3 KB
  508. 06_assumed-background.en.srt 10.3 KB
  509. 01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.srt 10.3 KB
  510. 01_motivating-mapreduce.en.srt 10.3 KB
  511. 01_training-error-assessing-loss-on-the-training-set.en.srt 10.3 KB
  512. 01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-assignment-validation-idx.json.zip 10.2 KB
  513. 03_finding-related-items.en.srt 10.2 KB
  514. 01_boosting-a-decision-stump_CLA08-NB02.ipynb.zip 10.2 KB
  515. 03_recommending-from-a-large-catalogue.en.srt 10.2 KB
  516. 03_assessing-performance-and-ridge-regression.en.srt 10.2 KB
  517. 04_example-recognizing-images.en.srt 10.2 KB
  518. 01_measuring-purity.en.srt 10.2 KB
  519. 02_kd-tree-representation.en.srt 10.2 KB
  520. 04_cost-function-intuition.en.txt 10.2 KB
  521. 04_computing-the-cost-of-a-d-dimensional-curve.en.srt 10.1 KB
  522. 04_the-capstone-and-an-example-intelligent-application.en.srt 10.1 KB
  523. 01_implementing-locality-sensitive-hashing-from-scratch_CLU02-NB02.ipynb.zip 10.1 KB
  524. 04_interpreting-the-multiple-regression-fit.en.srt 10.1 KB
  525. 05_agglomerative-clustering-details.en.srt 10.0 KB
  526. 04_approach-2-gradient-descent.en.srt 10.0 KB
  527. 01_recap-of-overfitting-regularization-in-logistic-regression.en.txt 808 bytes
  528. 03_vectorization-part-2.en.srt 10.0 KB
  529. 01_inference-in-code.en.srt 10.0 KB
  530. 01_exploring-different-multiple-regression-models-for-house-price-prediction_instructions.html 10.0 KB
  531. 01_performance-of-nn-as-amount-of-data-grows.en.srt 10.0 KB
  532. 03_collaborative-filtering-algorithm.en.txt 10.0 KB
  533. 01_logistic-regression-with-l2-regularization_important_words.json.zip 862 bytes
  534. 03_gradient-descent-intuition.en.srt 10.0 KB
  535. 02_module-1-recap.en.txt 10.0 KB
  536. 04_data-likelihood.en.srt 10.0 KB
  537. 05_greedy-algorithms.en.srt 9.9 KB
  538. 05_to-normalize-or-not-and-other-distance-considerations.en.srt 9.9 KB
  539. 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.srt 9.9 KB
  540. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  541. 04_distance-metrics-cosine-similarity.en.srt 9.8 KB
  542. 04_coordinate-descent-for-least-squares-regression-normalized-features.en.srt 9.8 KB
  543. 02_multiclass-classification-with-1-versus-all.en.srt 9.8 KB
  544. 03_intuition-behind-decision-trees.en.srt 2.2 KB
  545. 03_intuition-behind-decision-trees.en.txt 1.4 KB
  546. 06_approximate-k-nn-search-using-kd-trees.en.srt 9.8 KB
  547. 01_boosting-a-decision-stump_module-8-boosting-assignment-2-blank.ipynb.zip 9.8 KB
  548. 04_task-of-learning-decision-trees-from-data.en.txt 2.9 KB
  549. 03_where-we-see-recommender-systems-in-action.en.srt 9.7 KB
  550. 03_regression-fundamentals-data-model.en.srt 9.7 KB
  551. 01_1-nearest-neighbor-regression-approach.en.srt 9.7 KB
  552. 02_vectorization-part-1.en.srt 9.7 KB
  553. 01_other-applications-of-clustering.en.srt 9.7 KB
  554. 01_example-of-continuous-state-space-applications.en.srt 9.7 KB
  555. 04_word-count-representation-for-measuring-similarity.en.srt 9.6 KB
  556. 01_gradient-descent-implementation.en.srt 9.6 KB
  557. 04_choosing-the-learning-rate.en.srt 9.6 KB
  558. 02_trading-off-precision-and-recall.en.txt 9.6 KB
  559. 01_the-problem-of-overfitting.en.txt 9.6 KB
  560. 02_tensorflow-implementation-of-collaborative-filtering.en.txt 9.6 KB
  561. 02_linear-regression-model-part-2.en.srt 9.6 KB
  562. 01_making-predictions-with-decision-trees.en.srt 1.8 KB
  563. 01_making-predictions-with-decision-trees.en.txt 1.1 KB
  564. 03_regression-fundamentals-data-model.en.txt 5.9 KB
  565. 51 8 bytes
  566. 01_open-challenges-in-ml.mp4 33.3 MB
  567. 02_neurons-and-the-brain.en.txt 9.5 KB
  568. 04_estimating-cluster-parameters-from-soft-assignments.en.srt 9.5 KB
  569. 06_bias-variance-and-neural-networks.en.txt 9.5 KB
  570. 01_fitting-a-simple-linear-regression-model-on-housing-data_instructions.html 9.5 KB
  571. 06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt 9.4 KB
  572. 02_optional-picking-the-best-threshold-to-split-on.en.txt 2.5 KB
  573. 06_polynomial-regression.en.srt 9.4 KB
  574. 04_conditional-statements-and-loops-in-python.en.srt 9.4 KB
  575. 04_xgboost.en.srt 9.4 KB
  576. 02_learning-process.en.txt 9.4 KB
  577. 01_recap-of-decision-trees.en.srt 1.3 KB
  578. 01_recap-of-decision-trees.en.txt 804 bytes
  579. 01_finding-unusual-events.en.txt 9.4 KB
  580. 02_early-stopping-in-learning-decision-trees.en.srt 9.4 KB
  581. 01_computing-soft-assignments-from-known-cluster-parameters.en.srt 9.4 KB
  582. 01_reducing-the-number-of-features-optional.en.txt 9.4 KB
  583. 04_what-is-this-course-about.en.srt 9.4 KB
  584. 01_k-nearest-neighbors-regression.en.srt 9.3 KB
  585. 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.srt 9.3 KB
  586. 01_clustering-text-data-with-k-means_numpy.argmin.html 9.3 KB
  587. 04_training-evaluating-a-classifier-using-deep-features.en.srt 9.3 KB
  588. 01_diagnosing-bias-and-variance.en.txt 9.2 KB
  589. 07_specialization-overview.en.srt 9.2 KB
  590. 02_distance-metrics-euclidean-and-scaled-euclidean.en.srt 9.2 KB
  591. 04_developing-and-evaluating-an-anomaly-detection-system.en.txt 9.2 KB
  592. 03_bellman-equation.en.txt 9.2 KB
  593. 03_estimating-cluster-parameters-from-known-cluster-assignments.en.srt 9.2 KB
  594. 03_influence-of-high-leverage-points-removing-center-city.en.srt 9.2 KB
  595. 03_review-of-basics-of-conditional-probabilities.en.srt 9.2 KB
  596. 01_sparse-logistic-regression-with-l1-regularization.en.srt 9.2 KB
  597. 03_outline-of-second-half-of-course.en.srt 9.1 KB
  598. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  599. 04_boosting.en.srt 9.1 KB
  600. 02_a-review-of-overfitting.en.txt 2.2 KB
  601. 03_neural-network-with-softmax-output.en.srt 9.1 KB
  602. 05_gradient-descent-for-linear-regression.en.srt 9.1 KB
  603. 01_retrieving-wikipedia-articles-assignment_instructions.html 9.0 KB
  604. 05_bivariate-and-multivariate-gaussians.en.srt 9.0 KB
  605. 02_k-means-intuition.en.srt 9.0 KB
  606. 02_choosing-a-split-information-gain.en.txt 9.0 KB
  607. 01_modeling-text-topics-with-latent-dirichlet-allocation_CLU05-NB01.ipynb.zip 8.9 KB
  608. 06_deep-features.en.srt 8.9 KB
  609. 01_from-weighted-k-nn-to-kernel-regression.en.srt 8.9 KB
  610. 01_cost-function-for-logistic-regression.en.txt 8.9 KB
  611. 01_error-metrics-for-skewed-datasets.en.txt 8.9 KB
  612. 03_supervised-learning-part-2.en.srt 8.9 KB
  613. 04_matrix-multiplication-code.en.srt 8.8 KB
  614. 01_irreducible-error-and-bias.en.srt 8.8 KB
  615. 04_who-we-are.en.srt 8.8 KB
  616. 01_training-logistic-regression-via-stochastic-gradient-ascent_module-10-online-learning-assignment-blank.ipynb.zip 8.8 KB
  617. 01_mixture-of-gaussians.en.srt 8.8 KB
  618. 03_random-forest-algorithm.en.srt 8.7 KB
  619. 05_feature-by-feature-update.en.srt 8.7 KB
  620. 01_recap-of-overfitting-and-regularization-in-decision-trees.en.srt 1.7 KB
  621. 01_recap-of-overfitting-and-regularization-in-decision-trees.en.txt 1.0 KB
  622. 06_visualization-examples.en.srt 8.7 KB
  623. 01_clustering-and-similarity-ml-block-diagram.en.srt 8.7 KB
  624. 06_hidden-markov-models.en.txt 8.7 KB
  625. 05_regularized-logistic-regression.en.srt 8.7 KB
  626. 06_fairness-bias-and-ethics.en.txt 8.6 KB
  627. 01_evaluating-overfitting-via-training-test-split.en.srt 8.6 KB
  628. 03_pca-in-code-optional.en.txt 8.6 KB
  629. 01_decision-trees-in-practice_CLA06-NB01.ipynb.zip 8.6 KB
  630. 04_optimization-objective.en.txt 8.6 KB
  631. 05_continuous-valued-features.en.srt 8.6 KB
  632. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  633. 01_is-there-a-path-to-agi.en.txt 8.6 KB
  634. 04_module-3-recap.en.srt 8.6 KB
  635. 04_discovering-hidden-structure-by-matrix-factorization.en.srt 8.5 KB
  636. 04_overfitting-demo.en.srt 8.5 KB
  637. 05_very-optional-deriving-gradient-of-log-likelihood.en.srt 8.5 KB
  638. 05_review-of-key-concepts.en.srt 8.5 KB
  639. 03_aggregating-over-unknown-classes-in-an-image-dataset.en.srt 8.5 KB
  640. 03_false-positives-false-negatives-and-confusion-matrices.en.srt 8.5 KB
  641. 03_selecting-best-feature-to-split-on.en.srt 8.5 KB
  642. 03_nn-search-with-kd-trees.en.srt 8.5 KB
  643. 02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.srt 8.5 KB
  644. 01_what-we-covered-and-what-we-didn-t-cover.en.srt 8.5 KB
  645. 05_module-by-module-topics-covered.en.txt 8.4 KB
  646. 02_k-means-as-coordinate-descent.en.srt 8.4 KB
  647. 02_outline-of-first-half-of-course.en.srt 8.4 KB
  648. 01_recap-of-handling-missing-data.en.srt 2.2 KB
  649. 01_recap-of-handling-missing-data.en.txt 1.4 KB
  650. 04_learning-very-non-linear-features-with-neural-networks.en.txt 8.4 KB
  651. 03_anomaly-detection-algorithm.en.txt 8.4 KB
  652. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  653. 02_assessing-performance-intro.en.srt 760 bytes
  654. 52 56 bytes
  655. 03_adding-data.mp4 32.9 MB
  656. 02_retrieving-wikipedia-articles_exam.html 8.3 KB
  657. 03_checking-gradient-descent-for-convergence.en.srt 8.3 KB
  658. 01_computing-the-gradient-of-rss.en.srt 8.3 KB
  659. 02_regularization-and-bias-variance.en.txt 8.3 KB
  660. 01_state-action-value-function-definition.en.txt 8.3 KB
  661. 04_computing-exploring-tf-idfs.en.srt 8.3 KB
  662. 03_establishing-a-baseline-level-of-performance.en.txt 8.3 KB
  663. 01_adaboost-overview.en.txt 2.4 KB
  664. 02_variance-and-the-bias-variance-tradeoff.en.srt 8.3 KB
  665. 02_data-in-tensorflow.en.txt 8.3 KB
  666. 03_whats-ahead-in-the-specialization.en.srt 8.2 KB
  667. 01_implementing-binary-decision-trees_CLA05-NB02.ipynb.zip 8.2 KB
  668. 11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.srt 8.2 KB
  669. 01_decision-trees-in-practice_module-6-decision-tree-practical-assignment-blank.zip 8.2 KB
  670. 01_visualizing-the-ridge-cost.en.srt 8.2 KB
  671. 03_precision-recall-curves.en.srt 8.2 KB
  672. 03_creating-variables-in-python.en.srt 8.2 KB
  673. 03_the-return-in-reinforcement-learning.en.txt 8.2 KB
  674. 05_normalizing-weights.en.srt 2.7 KB
  675. 05_normalizing-weights.en.txt 1.7 KB
  676. 04_effect-of-coefficient-values-on-predicted-probabilities.en.srt 8.2 KB
  677. 03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.srt 8.1 KB
  678. 02_evaluating-a-model.en.txt 8.1 KB
  679. 04_k-nn-algorithm.en.srt 8.1 KB
  680. 03_visualizing-the-lasso-cost-and-solution.en.srt 8.1 KB
  681. 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.srt 8.1 KB
  682. 04_mapreduce-for-k-means.en.srt 8.0 KB
  683. 02_convergence-initialization-and-overfitting-of-em.en.txt 8.0 KB
  684. 01_what-is-reinforcement-learning.en.txt 8.0 KB
  685. 02_softmax.en.txt 7.9 KB
  686. 01_threshold-splits-for-continuous-inputs.en.srt 7.9 KB
  687. 01_the-k-means-algorithm.en.srt 7.9 KB
  688. 02_loading-exploring-house-sale-data.en.srt 7.9 KB
  689. 04_finding-the-max-via-hill-climbing.en.srt 7.9 KB
  690. 02_the-cost-of-using-a-given-line.en.srt 7.9 KB
  691. 02_gaussian-normal-distribution.en.txt 7.9 KB
  692. 01_motivations.en.txt 7.8 KB
  693. 01_application-of-deep-learning-to-computer-vision.en.srt 7.8 KB
  694. 03_using-random-lines-to-partition-points.en.srt 7.8 KB
  695. 03_precision-recall-curve.en.srt 7.8 KB
  696. 06_outlining-the-second-half-of-the-course.en.srt 7.8 KB
  697. 02_rewriting-the-single-observation-model-in-vector-notation.en.srt 7.8 KB
  698. 01_regression-ml-block-diagram.en.srt 7.8 KB
  699. 03_putting-it-together.en.txt 7.8 KB
  700. 02_using-per-item-features.en.txt 7.8 KB
  701. 02_lunar-lander.en.srt 7.8 KB
  702. 05_deciding-what-to-try-next-revisited.en.txt 7.8 KB
  703. 01_implementing-binary-decision-trees_module-5-decision-tree-assignment-2-blank.ipynb.zip 7.7 KB
  704. 06_regression-trees-optional.en.txt 7.7 KB
  705. 03_the-lasso-objective-and-its-coefficient-path.en.srt 7.7 KB
  706. 01_review-of-gradient-ascent.en.srt 7.7 KB
  707. 05_full-cycle-of-a-machine-learning-project.en.txt 7.7 KB
  708. 03_ensemble-classifiers.en.srt 7.7 KB
  709. 06_choosing-stepsize-and-convergence-criteria.en.srt 7.7 KB
  710. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  711. 02_simplified-cost-function-for-logistic-regression.en.srt 7.6 KB
  712. 03_why-do-we-need-activation-functions.en.srt 7.6 KB
  713. 01_choosing-features-and-metrics-for-nearest-neighbor-search_CLU02-NB01.ipynb.zip 7.6 KB
  714. 01_em-iterates-in-equations-and-pictures.en.srt 7.6 KB
  715. 01_balancing-fit-and-magnitude-of-coefficients.en.srt 7.6 KB
  716. 01_open-challenges-in-ml.en.txt 7.6 KB
  717. 03_how-to-handle-the-intercept.en.srt 7.6 KB
  718. 01_linear-regression-model-part-1.en.txt 7.6 KB
  719. 03_an-unsupervised-task.en.srt 7.6 KB
  720. 02_starting-a-jupyter-notebook.en.srt 7.6 KB
  721. 01_choosing-step-size.en.srt 7.6 KB
  722. 02_recall-fraction-of-positive-data-predicted-to-be-positive.en.txt 2.7 KB
  723. 01_feature-scaling-part-1.en.srt 7.6 KB
  724. 05_algorithm-refinement-greedy-policy.en.txt 7.5 KB
  725. 01_precision-recall-extremes.en.txt 2.2 KB
  726. 05_ridge-regression-demo.en.txt 7.5 KB
  727. 01_precision-fraction-of-positive-predictions-that-are-actually-positive.en.srt 7.5 KB
  728. 02_optional-pruning-decision-trees-to-avoid-overfitting.en.srt 7.5 KB
  729. 04_other-examples-of-clustering.en.srt 7.5 KB
  730. 01_document-representation.en.srt 7.5 KB
  731. 02_applications-of-machine-learning.en.srt 7.5 KB
  732. 01_logistic-regression-with-l2-regularization_CLA04-NB01.ipynb.zip 7.5 KB
  733. 01_recap-of-precision-recall.en.srt 2.1 KB
  734. 01_recap-of-precision-recall.en.txt 1.3 KB
  735. 02_gibbs-sampling-from-10-000-feet.en.srt 7.4 KB
  736. 01_collaborative-filtering-vs-content-based-filtering.en.txt 7.4 KB
  737. 02_deep-learning-for-content-based-filtering.en.txt 7.4 KB
  738. 08_exploring-other-features-of-the-data.en.srt 7.4 KB
  739. 03_k-means-algorithm.en.txt 7.4 KB
  740. 04_unsupervised-learning-part-1.en.txt 7.4 KB
  741. 03_visualizing-decision-boundaries.en.srt 7.3 KB
  742. 03_overfitting-in-decision-trees.en.srt 7.3 KB
  743. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  744. 02_review-of-basics-of-probabilities.en.srt 7.3 KB
  745. 06_jupyter-notebooks.en.srt 7.3 KB
  746. 03_larger-neural-network-example-optional.en.txt 7.3 KB
  747. 06_running-gradient-descent.en.srt 7.3 KB
  748. 04_what-happens-after-deployment.en.txt 7.3 KB
  749. 02_k-fold-cross-validation.en.srt 7.3 KB
  750. 05_tensorflow-implementation-of-content-based-filtering.en.srt 7.3 KB
  751. 03_decision-boundary.en.txt 7.3 KB
  752. 01_why-gradient-ascent-won-t-scale.en.txt 2.9 KB
  753. 02_additional-layer-types.en.txt 7.2 KB
  754. 02_choosing-activation-functions.en.txt 7.2 KB
  755. 02_stochastic-gradient-learning-one-data-point-at-a-time.en.txt 2.7 KB
  756. 05_outlining-the-first-half-of-the-course.en.srt 7.2 KB
  757. 02_finding-maxima-or-minima-analytically.en.srt 7.2 KB
  758. 04_examples-of-classification-tasks.en.srt 7.2 KB
  759. 01_making-recommendations.en.srt 7.2 KB
  760. 01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy.ndarray.shape.html 7.2 KB
  761. 03_exploring-word-counts.en.srt 7.2 KB
  762. 01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.en.srt 7.2 KB
  763. 02_convergence-paths.en.srt 2.8 KB
  764. 02_convergence-paths.en.txt 1.7 KB
  765. 03_all-subsets.en.srt 7.2 KB
  766. 01_shuffle-data-before-running-stochastic-gradient.en.srt 3.0 KB
  767. 01_shuffle-data-before-running-stochastic-gradient.en.txt 1.9 KB
  768. 03_a-standard-gibbs-sampler-for-lda.en.txt 7.2 KB
  769. 02_feature-split-selection-with-missing-data.en.srt 7.2 KB
  770. 02_choosing-step-size.en.txt 2.8 KB
  771. 03_cost-function-with-regularization.en.txt 7.2 KB
  772. 03_don-t-trust-last-coefficients.en.srt 2.2 KB
  773. 03_don-t-trust-last-coefficients.en.txt 1.4 KB
  774. 05_linear-classifiers.en.srt 7.2 KB
  775. 05_anomaly-detection-vs-supervised-learning.en.txt 7.1 KB
  776. 04_learning-curves.en.srt 7.1 KB
  777. 01_exploring-ensemble-methods_CLA08-NB01.ipynb.zip 7.1 KB
  778. 03_example-of-computing-derivative-for-logistic-regression.en.srt 7.1 KB
  779. 03_matrix-multiplication-rules.en.txt 7.1 KB
  780. 04_improved-implementation-of-softmax.en.txt 7.1 KB
  781. 02_error-analysis.en.txt 7.0 KB
  782. 06_optional-adding-regularization.en.txt 2.5 KB
  783. 02_coordinate-descent.en.srt 7.0 KB
  784. 01_logistic-regression-with-l2-regularization_module-4-linear-classifier-regularization-assignment-blank.ipynb.zip 7.0 KB
  785. 01_a-performance-metric-for-recommender-systems.en.srt 7.0 KB
  786. 02_logistic-regression.en.txt 7.0 KB
  787. 01_penalizing-large-coefficients-to-mitigate-overfitting.en.srt 7.0 KB
  788. 04_the-dendrogram.en.srt 7.0 KB
  789. 03_inference-making-predictions-forward-propagation.en.srt 7.0 KB
  790. 01_scaling-to-huge-datasets-through-parallelization-module-recap.en.srt 2.1 KB
  791. 01_scaling-to-huge-datasets-through-parallelization-module-recap.en.txt 1.3 KB
  792. 05_initializing-k-means.en.txt 7.0 KB
  793. 01_multiple-features.en.txt 6.9 KB
  794. 02_overfitting-in-boosting.en.srt 6.9 KB
  795. 04_approach-2-gradient-descent.en.srt 6.9 KB
  796. 01_implementing-logistic-regression-from-scratch_CLA03-NB01.ipynb.zip 6.9 KB
  797. 01_training-logistic-regression-via-stochastic-gradient-ascent_important_words.json.zip 862 bytes
  798. 04_random-stochastic-environment-optional.en.txt 6.9 KB
  799. 04_learning-rate.en.txt 6.9 KB
  800. 01_predicting-class-probabilities-with-generalized-linear-models.en.srt 6.9 KB
  801. 03_building-a-neural-network.en.txt 6.9 KB
  802. 04_linear-regression-a-model-based-approach.en.srt 6.9 KB
  803. 01_important-update-regarding-the-machine-learning-specialization_instructions.html 1.8 KB
  804. 02_slides-presented-in-this-module_instructions.html 1.1 KB
  805. 06_why-a-case-study-approach.en.txt 6.9 KB
  806. 02_addressing-overfitting.en.txt 6.9 KB
  807. 02_motiving-probabilistic-clustering-models.en.txt 6.9 KB
  808. 05_linear-classifier-model.en.srt 6.8 KB
  809. 01_neural-network-layer.en.txt 6.8 KB
  810. 02_thresholding-ridge-coefficients.en.srt 6.8 KB
  811. 01_alternatives-to-the-sigmoid-activation.en.srt 6.8 KB
  812. 02_visualizing-the-ridge-solution.en.srt 6.8 KB
  813. 03_optional-another-perspecting-on-overfitting-in-logistic-regression.en.txt 6.8 KB
  814. 05_searching-neighboring-bins.en.txt 6.8 KB
  815. 03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.srt 6.8 KB
  816. 02_interpreting-the-mixture-of-gaussian-terms.en.srt 6.8 KB
  817. 01_collaborative-filtering-people-who-bought-this-also-bought.en.srt 6.8 KB
  818. 02_multiple-regression-intro.en.srt 643 bytes
  819. 53 31 bytes
  820. 04_learning-very-non-linear-features-with-neural-networks.mp4 32.8 MB
  821. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  822. 03_training-evaluating-a-classifier-using-raw-image-pixels.en.srt 6.8 KB
  823. 01_example-of-adaboost-in-action.en.srt 6.8 KB
  824. 02_retrieval-as-k-nearest-neighbor-search.en.txt 2.7 KB
  825. 02_state-action-value-function-example.en.srt 6.8 KB
  826. 01_deciding-what-to-try-next.en.srt 6.8 KB
  827. 03_1-nn-algorithm.en.txt 2.3 KB
  828. 05_classification-with-multiple-outputs-optional.en.srt 6.8 KB
  829. 04_modeling-seasonality.en.txt 6.8 KB
  830. 01_the-need-for-bayesian-inference.en.srt 6.7 KB
  831. 04_assessing-the-quality-and-choosing-the-number-of-clusters.en.txt 6.7 KB
  832. 02_weighted-error.en.srt 6.7 KB
  833. 01_mean-normalization.en.txt 6.7 KB
  834. 05_goal-of-lda-inference.en.srt 6.7 KB
  835. 03_using-the-fitted-line.en.srt 6.7 KB
  836. 05_module-4-recap.en.txt 6.7 KB
  837. 03_error-vs-amount-of-data.en.srt 6.7 KB
  838. 01_ensemble-methods-impact-of-boosting-quick-recap.en.srt 6.7 KB
  839. 03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.txt 2.6 KB
  840. 04_binary-labels-favs-likes-and-clicks.en.txt 6.7 KB
  841. 01_principle-of-occams-razor-learning-simpler-decision-trees.en.srt 6.7 KB
  842. 03_mapreduce-execution-overview-and-combiners.en.srt 6.6 KB
  843. 04_strategy-2-purification-by-imputing-missing-data.en.srt 6.6 KB
  844. 04_using-one-hot-encoding-of-categorical-features.en.srt 6.6 KB
  845. 02_where-is-ml-going.en.txt 6.6 KB
  846. 02_approach-1-closed-form-solution.en.srt 6.6 KB
  847. 06_assumed-background.en.txt 6.6 KB
  848. 01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.srt 6.6 KB
  849. 02_generalization-error-what-we-really-want.en.txt 6.5 KB
  850. 03_assessing-performance-and-ridge-regression.en.txt 6.5 KB
  851. 05_agglomerative-clustering-details.en.txt 6.5 KB
  852. 04_interpreting-derivative-for-logistic-regression.en.srt 6.5 KB
  853. 02_recommendations-from-known-user-item-features.en.srt 6.5 KB
  854. 02_sampling-with-replacement.en.srt 6.5 KB
  855. 01_training-error-assessing-loss-on-the-training-set.en.txt 6.5 KB
  856. 04_interpreting-the-fitted-line.en.srt 6.5 KB
  857. 02_simple-and-multiple-regression.en.srt 6.5 KB
  858. 05_lasso-demo.en.srt 6.5 KB
  859. 04_overfitting-in-classification.en.srt 6.5 KB
  860. 01_predicting-sentiment-from-product-reviews_CLA02-NB01.ipynb.zip 6.5 KB
  861. 01_complexity-of-brute-force-search.en.srt 2.7 KB
  862. 01_complexity-of-brute-force-search.en.txt 1.6 KB
  863. 04_complexity-of-nn-search-with-kd-trees.en.srt 6.5 KB
  864. 01_the-matrix-completion-task.en.srt 6.4 KB
  865. 01_iterative-loop-of-ml-development.en.txt 6.4 KB
  866. 01_using-multiple-decision-trees.en.srt 6.4 KB
  867. 01_training-validation-test-split-for-model-selection-fitting-and-assessment.en.txt 6.4 KB
  868. 01_modifying-decision-trees-to-handle-missing-data.en.srt 6.4 KB
  869. 04_reweighing-data-to-focus-on-mistakes.en.srt 6.4 KB
  870. 01_implementing-logistic-regression-from-scratch_module-3-linear-classifier-learning-assignment-blank.ipynb.zip 6.4 KB
  871. 05_to-normalize-or-not-and-other-distance-considerations.en.txt 6.4 KB
  872. 04_interpreting-the-multiple-regression-fit.en.txt 6.4 KB
  873. 01_gradient-descent.en.txt 6.4 KB
  874. 02_matrix-multiplication.en.txt 6.4 KB
  875. 03_recommending-from-a-large-catalogue.en.txt 6.4 KB
  876. 05_using-apply-for-data-transformation.en.srt 6.4 KB
  877. 01_optional-motivating-pruning.en.txt 6.4 KB
  878. 04_creating-evaluating-a-personalized-song-recommender.en.srt 6.4 KB
  879. 04_regularized-linear-regression.en.txt 6.4 KB
  880. 05_visualizing-the-cost-function.en.txt 6.3 KB
  881. 02_the-resulting-ridge-objective-and-its-extreme-solutions.en.srt 6.3 KB
  882. 01_exploring-precision-and-recall_CLA09-NB01.ipynb.zip 6.3 KB
  883. 01_gradients-derivatives-in-multiple-dimensions.en.srt 6.3 KB
  884. 01_performance-of-nn-as-amount-of-data-grows.en.txt 6.3 KB
  885. 02_kd-tree-representation.en.txt 6.3 KB
  886. 01_motivating-mapreduce.en.txt 6.3 KB
  887. 03_timeline-of-scalable-machine-learning-stochastic-gradient.en.srt 6.3 KB
  888. 03_what-is-an-intelligent-restaurant-review-system.en.srt 6.3 KB
  889. 03_cost-function-formula.en.txt 6.2 KB
  890. 04_defining-more-bins.en.txt 2.6 KB
  891. 01_identifying-safe-loans-with-decision-trees_CLA05-NB01.ipynb.zip 6.2 KB
  892. 04_the-capstone-and-an-example-intelligent-application.en.txt 6.2 KB
  893. 02_divisive-clustering.en.srt 6.2 KB
  894. 02_gradient-descent-multidimensional-hill-descent.en.srt 6.2 KB
  895. 02_learning-boosted-decision-stumps-with-adaboost.en.srt 6.2 KB
  896. 03_what-do-we-mean-by-loss.en.srt 6.2 KB
  897. 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.txt 6.2 KB
  898. 03_discussing-the-closed-form-solution.en.srt 6.2 KB
  899. 06_approximate-k-nn-search-using-kd-trees.en.txt 6.2 KB
  900. 02_loading-and-exploring-song-data.en.srt 6.2 KB
  901. 04_when-to-stop-recursing.en.srt 6.2 KB
  902. 03_where-we-see-recommender-systems-in-action.en.txt 6.2 KB
  903. 04_distance-metrics-cosine-similarity.en.txt 6.2 KB
  904. 02_predicting-loan-defaults-with-decision-trees.en.srt 6.2 KB
  905. 01_other-applications-of-clustering.en.txt 6.1 KB
  906. 02_optional-responsibilities-as-bayes-rule.en.srt 6.1 KB
  907. 01_how-neural-networks-are-implemented-efficiently.en.srt 6.1 KB
  908. 05_greedy-algorithms.en.txt 6.1 KB
  909. 01_encoding-categorical-inputs.en.srt 6.1 KB
  910. 03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.txt 6.1 KB
  911. 04_estimating-cluster-parameters-from-soft-assignments.en.txt 6.1 KB
  912. 01_a-brief-recap.en.txt 2.4 KB
  913. 03_computing-coefficient-of-each-ensemble-component.en.srt 6.1 KB
  914. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  915. 05_adding-higher-order-effects.en.srt 6.1 KB
  916. 02_distance-metrics-euclidean-and-scaled-euclidean.en.txt 6.1 KB
  917. 02_more-complex-neural-networks.en.txt 6.1 KB
  918. 01_forward-prop-in-a-single-layer.en.srt 6.1 KB
  919. 02_lsh-as-an-alternative-to-kd-trees.en.srt 6.1 KB
  920. 02_multiclass-classification-with-1-versus-all.en.txt 6.1 KB
  921. 04_computing-the-cost-of-a-d-dimensional-curve.en.txt 6.0 KB
  922. 01_1-nearest-neighbor-regression-approach.en.txt 6.0 KB
  923. 04_xgboost.en.txt 6.0 KB
  924. 04_building-a-recommender-system-via-classification.en.srt 6.0 KB
  925. 03_an-alternative-document-clustering-model.en.srt 6.0 KB
  926. 02_general-implementation-of-forward-propagation.en.txt 6.0 KB
  927. 01_motivating-the-use-of-multiple-inputs.en.srt 6.0 KB
  928. 04_approach-2-gradient-descent.en.txt 6.0 KB
  929. 04_word-count-representation-for-measuring-similarity.en.txt 6.0 KB
  930. 04_data-likelihood.en.txt 6.0 KB
  931. 02_trading-off-precision-and-recall.en.srt 6.0 KB
  932. 01_training-and-evaluating-a-classifier.en.srt 6.0 KB
  933. 04_feature-selection-lasso-and-nearest-neighbor-regression.en.srt 6.0 KB
  934. 01_computing-soft-assignments-from-known-cluster-parameters.en.txt 6.0 KB
  935. 06_calculating-tf-idf-vectors.en.srt 6.0 KB
  936. 02_l2-regularized-logistic-regression.en.srt 6.0 KB
  937. 04_coordinate-descent-for-least-squares-regression-normalized-features.en.txt 5.9 KB
  938. 01_tensorflow-implementation.en.srt 5.9 KB
  939. 07_specialization-overview.en.txt 5.9 KB
  940. 01_observing-effects-of-l2-penalty-in-polynomial-regression_REG04-NB01.ipynb.zip 5.9 KB
  941. 04_gradient-descent-for-multiple-linear-regression.en.txt 5.9 KB
  942. 01_what-is-clustering.en.srt 5.9 KB
  943. 01_k-nearest-neighbors-regression.en.txt 5.9 KB
  944. 07_assumed-background.en.srt 5.8 KB
  945. 09_exploring-the-most-positive-negative-aspects-of-a-product.en.srt 5.8 KB
  946. 06_choosing-the-number-of-clusters.en.txt 5.8 KB
  947. 04_querying-the-nearest-neighbors-model-to-retrieve-images.en.srt 5.8 KB
  948. 01_clustering-text-data-with-k-means_pyplot_api.html 330 bytes
  949. 02_early-stopping-in-learning-decision-trees.en.txt 5.8 KB
  950. 04_what-is-this-course-about.en.txt 5.8 KB
  951. 05_regression-ml-block-diagram.en.srt 5.8 KB
  952. 05_unsupervised-learning-part-2.en.srt 5.8 KB
  953. 01_recommender-systems-ml-block-diagram.en.srt 5.8 KB
  954. 03_intuition-behind-maximum-likelihood-estimation.en.srt 5.8 KB
  955. 01_recursive-greedy-algorithm.en.srt 5.8 KB
  956. 02_using-stochastic-gradient-for-online-learning.en.srt 5.8 KB
  957. 04_boosting.en.txt 5.8 KB
  958. 03_neural-network-with-softmax-output.en.txt 5.8 KB
  959. 01_computing-the-gradient-of-the-ridge-objective.en.srt 5.7 KB
  960. 03_outline-of-second-half-of-course.en.txt 5.7 KB
  961. 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.txt 5.7 KB
  962. 02_distance-metrics.en.srt 5.7 KB
  963. 03_influence-of-high-leverage-points-removing-center-city.en.txt 5.7 KB
  964. 05_computing-distances-between-wikipedia-articles.en.srt 5.7 KB
  965. 01_coordinate-descent-for-lasso-normalized-features.en.srt 5.7 KB
  966. 05_bivariate-and-multivariate-gaussians.en.txt 5.7 KB
  967. 02_a-brief-recap.en.srt 1.9 KB
  968. 02_a-brief-recap.en.txt 1.2 KB
  969. 03_estimating-cluster-parameters-from-known-cluster-assignments.en.txt 5.7 KB
  970. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  971. 03_comparing-gradient-to-stochastic-gradient.en.srt 5.7 KB
  972. 07_evaluating-a-classifier-the-roc-curve.en.srt 5.7 KB
  973. 03_what-is-a-visual-product-recommender.en.srt 5.7 KB
  974. 02_influence-of-high-leverage-points-exploring-the-data.en.srt 5.7 KB
  975. 02_k-means-intuition.en.txt 5.7 KB
  976. 01_implementing-lasso-using-coordinate-descent_REG05-NB02.ipynb.zip 5.7 KB
  977. 02_implementing-gradient-descent-for-multiple-regression_REG02-NB02.ipynb.zip 5.6 KB
  978. 02_the-feature-selection-task.en.srt 5.6 KB
  979. 04_univariate-gaussian-distributions.en.txt 2.4 KB
  980. 01_decision-tree-model.en.txt 5.6 KB
  981. 06_deep-features.en.txt 5.6 KB
  982. 04_module-3-recap.en.txt 5.6 KB
  983. 04_using-the-output-of-collapsed-gibbs-sampling.en.srt 5.6 KB
  984. 01_from-weighted-k-nn-to-kernel-regression.en.txt 5.6 KB
  985. 02_supervised-learning-part-1.en.txt 5.6 KB
  986. 01_sparse-logistic-regression-with-l1-regularization.en.txt 5.6 KB
  987. 02_the-general-mapreduce-abstraction.en.srt 5.6 KB
  988. 02_case-study-where-accuracy-is-not-best-metric-for-classification.en.srt 5.6 KB
  989. 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.srt 5.6 KB
  990. 03_weighted-k-nearest-neighbors.en.srt 5.6 KB
  991. 02_the-sigmoid-or-logistic-link-function.en.srt 5.6 KB
  992. 02_feature-scaling-part-2.en.txt 5.6 KB
  993. 01_mixture-of-gaussians.en.txt 5.6 KB
  994. 05_when-to-use-decision-trees.en.txt 5.6 KB
  995. 03_random-forest-algorithm.en.txt 5.6 KB
  996. 02_who-is-this-specialization-for.en.srt 5.6 KB
  997. 03_review-of-basics-of-conditional-probabilities.en.txt 5.6 KB
  998. 01_advanced-optimization.en.txt 5.6 KB
  999. 02_challenge-of-missing-data.en.srt 5.5 KB
  1000. 02_visualizing-overconfident-predictions.en.srt 5.5 KB
  1001. 04_exploring-the-most-popular-product.en.srt 5.5 KB
  1002. 01_how-we-got-into-ml.en.srt 5.5 KB
  1003. 02_clustering-documents-an-unsupervised-learning-task.en.srt 5.5 KB
  1004. 01_modeling-text-topics-with-latent-dirichlet-allocation_instructions.html 5.5 KB
  1005. 02_training-test-curves.en.srt 5.5 KB
  1006. 01_clustering-text-data-with-gaussian-mixtures_CLU04-NB02.ipynb.zip 5.5 KB
  1007. 03_supervised-learning-part-2.en.txt 5.5 KB
  1008. 03_creating-evaluating-a-popularity-based-song-recommender.en.srt 5.5 KB
  1009. 05_continuous-valued-features.en.txt 5.5 KB
  1010. 02_approach-1-closed-form-solution.en.srt 5.5 KB
  1011. 01_irreducible-error-and-bias.en.txt 5.5 KB
  1012. 03_relationship-to-k-means.en.txt 2.6 KB
  1013. 01_the-boosting-theorem.en.srt 5.5 KB
  1014. 01_modeling-text-data-with-a-hierarchy-of-clusters_CLU06-NB01.ipynb.zip 5.5 KB
  1015. 01_a-brief-recap.en.srt 2.4 KB
  1016. 01_a-brief-recap.en.txt 1.5 KB
  1017. 01_evaluating-overfitting-via-training-test-split.en.txt 5.5 KB
  1018. 01_welcome.en.srt 5.5 KB
  1019. 10_applying-learned-models-to-predict-price-of-an-average-house.en.srt 5.5 KB
  1020. 04_assumed-background.en.srt 5.5 KB
  1021. 03_what-is-good-performance-for-a-classifier.en.srt 5.5 KB
  1022. 06_decision-boundaries.en.srt 5.4 KB
  1023. 04_what-is-the-course-about.en.srt 5.4 KB
  1024. 03_aggregating-over-unknown-classes-in-an-image-dataset.en.txt 5.4 KB
  1025. 02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.en.txt 5.4 KB
  1026. 04_hope-for-unsupervised-learning-and-some-challenge-cases.en.srt 5.4 KB
  1027. 03_finding-related-items.en.txt 5.4 KB
  1028. 03_what-is-the-document-retrieval-task.en.srt 2.1 KB
  1029. 01_clustering-text-data-with-gaussian-mixtures_em_utilities.py.zip 2.8 KB
  1030. 01_measuring-purity.en.txt 5.4 KB
  1031. 05_machine-learning-is-changing-the-world.en.srt 5.4 KB
  1032. 05_visualizing-scaling-behavior-of-kd-trees.en.srt 5.4 KB
  1033. 05_optional-measuring-convergence.en.srt 5.4 KB
  1034. 02_starting-turi-create-loading-an-sframe.en.srt 5.4 KB
  1035. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1036. 01_welcome.en.srt 5.4 KB
  1037. 04_training-evaluating-a-classifier-using-deep-features.en.txt 5.4 KB
  1038. 06_visualizing-predictions-of-simple-model-with-matplotlib.en.srt 5.3 KB
  1039. 02_loading-exploring-wikipedia-data.en.srt 5.3 KB
  1040. 01_what-we-covered-and-what-we-didn-t-cover.en.txt 5.3 KB
  1041. 04_overfitting-demo.en.txt 5.3 KB
  1042. 02_k-means-as-coordinate-descent.en.txt 5.3 KB
  1043. 01_clustering-and-similarity-ml-block-diagram.en.txt 5.3 KB
  1044. 04_components-of-latent-dirichlet-allocation-model.en.txt 2.0 KB
  1045. 02_mars-rover-example.en.txt 5.3 KB
  1046. 04_example-recognizing-images.en.txt 5.3 KB
  1047. 03_strategy-1-purification-by-skipping-missing-data.en.srt 5.3 KB
  1048. 01_acknowledgments_instructions.html 5.3 KB
  1049. 01_acknowledgements_instructions.html 5.3 KB
  1050. 01_predicting-house-prices-using-k-nearest-neighbors-regression_REG06-NB01.ipynb.zip 5.3 KB
  1051. 04_conditional-statements-and-loops-in-python.en.txt 5.3 KB
  1052. 03_deploying-an-ml-service.en.srt 5.3 KB
  1053. 01_acknowledgments_instructions.html 5.3 KB
  1054. 02_careful-with-step-sizes-that-are-too-large.en.srt 5.3 KB
  1055. 03_gradient-descent-intuition.en.txt 5.3 KB
  1056. 01_summary-and-thank-you.en.srt 5.3 KB
  1057. 03_vectorization-part-2.en.txt 5.3 KB
  1058. 03_smart-initialization-via-k-means.en.srt 5.3 KB
  1059. 01_what-is-collapsed-gibbs-sampling.en.txt 2.9 KB
  1060. 04_course-overview.en.srt 5.3 KB
  1061. 02_limitations-of-parametric-regression.en.srt 5.3 KB
  1062. 03_discussing-the-closed-form-solution.en.srt 5.2 KB
  1063. 01_inference-in-code.en.txt 5.2 KB
  1064. 03_whats-ahead-in-the-specialization.en.txt 5.2 KB
  1065. 03_polynomial-regression.en.srt 5.2 KB
  1066. 01_limitations-of-kd-trees.en.srt 5.2 KB
  1067. 04_interacting-with-columns-of-an-sframe.en.srt 5.2 KB
  1068. 01_selecting-tuning-parameters-via-cross-validation.en.srt 5.2 KB
  1069. 02_the-goal-of-clustering.en.srt 5.2 KB
  1070. 01_a-brief-recap.en.srt 2.4 KB
  1071. 01_a-brief-recap.en.txt 1.5 KB
  1072. 06_lsh-in-higher-dimensions.en.srt 5.2 KB
  1073. 03_canvas-for-data-visualization.en.srt 5.2 KB
  1074. 03_intuition-behind-linear-classifiers.en.srt 5.2 KB
  1075. 04_who-we-are.en.txt 5.2 KB
  1076. 03_precision-recall-curves.en.txt 5.2 KB
  1077. 03_false-positives-false-negatives-and-confusion-matrices.en.txt 5.2 KB
  1078. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1079. 08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.srt 5.2 KB
  1080. 01_the-online-learning-task.en.srt 5.2 KB
  1081. 01_can-we-use-regularization-for-feature-selection.en.srt 5.1 KB
  1082. 04_discovering-hidden-structure-by-matrix-factorization.en.txt 5.1 KB
  1083. 03_selecting-best-feature-to-split-on.en.txt 5.1 KB
  1084. 04_k-nn-algorithm.en.txt 5.1 KB
  1085. 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.srt 5.1 KB
  1086. 05_very-optional-deriving-gradient-of-log-likelihood.en.txt 5.1 KB
  1087. 06_outlining-the-second-half-of-the-course.en.txt 5.1 KB
  1088. 02_learning-a-decision-stump.en.srt 5.1 KB
  1089. 02_vectorization-part-1.en.txt 5.1 KB
  1090. 01_why-would-stochastic-gradient-ever-work.en.srt 5.1 KB
  1091. 03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.en.txt 5.1 KB
  1092. 01_why-hierarchical-clustering.en.txt 2.4 KB
  1093. 04_choosing-the-learning-rate.en.txt 5.1 KB
  1094. 05_feature-by-feature-update.en.txt 5.0 KB
  1095. 05_overfitting-for-more-general-multiple-regression-models.en.srt 5.0 KB
  1096. 06_algorithmic-summary-of-gradient-descent-approach.en.srt 5.0 KB
  1097. 01_example-of-continuous-state-space-applications.en.txt 5.0 KB
  1098. 03_agglomerative-clustering.en.txt 2.5 KB
  1099. 01_gradient-descent-implementation.en.txt 5.0 KB
  1100. 02_linear-regression-model-part-2.en.txt 5.0 KB
  1101. 05_defining-which-reviews-have-positive-or-negative-sentiment.en.srt 5.0 KB
  1102. 05_visualizing-overfitting-with-high-degree-polynomial-features.en.srt 5.0 KB
  1103. 04_other-regression-examples.en.srt 5.0 KB
  1104. 06_polynomial-regression.en.txt 5.0 KB
  1105. 04_optional-learning-from-batches-of-data.en.srt 5.0 KB
  1106. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB01.ipynb.zip 5.0 KB
  1107. 03_using-random-lines-to-partition-points.en.txt 5.0 KB
  1108. 02_variance-and-the-bias-variance-tradeoff.en.txt 5.0 KB
  1109. 01_what-is-machine-learning.en.txt 5.0 KB
  1110. 01_modeling-text-data-with-a-hierarchy-of-clusters_em_utilities.py.zip 2.8 KB
  1111. 03_what-is-the-goal-and-how-might-you-naively-address-it.en.srt 4.9 KB
  1112. 04_effect-of-coefficient-values-on-predicted-probabilities.en.txt 4.9 KB
  1113. 01_computing-the-gradient-of-rss.en.txt 4.9 KB
  1114. 01_application-of-deep-learning-to-computer-vision.en.txt 4.9 KB
  1115. 03_regression-with-features-of-multiple-inputs.en.srt 4.9 KB
  1116. 02_lunar-lander.en.txt 4.9 KB
  1117. 01_regression-ml-block-diagram.en.txt 4.9 KB
  1118. 03_k-means-a-clustering-algorithm.en.srt 4.9 KB
  1119. 02_a-worked-example-for-lda-initial-setup.en.srt 4.9 KB
  1120. 01_what-we-didn-t-cover.en.txt 2.6 KB
  1121. 05_gradient-descent-for-linear-regression.en.txt 4.9 KB
  1122. 02_thank-you.en.srt 2.6 KB
  1123. 02_thank-you.en.txt 1.4 KB
  1124. 02_k-nearest-neighbors-in-practice.en.srt 4.9 KB
  1125. 01_important-update-regarding-the-machine-learning-specialization_instructions.html 1.8 KB
  1126. 02_slides-presented-in-this-module_instructions.html 1.1 KB
  1127. 02_the-cost-of-using-a-given-line.en.txt 4.9 KB
  1128. 03_welcome-to-this-course-and-specialization.en.srt 1.2 KB
  1129. 01_threshold-splits-for-continuous-inputs.en.txt 4.9 KB
  1130. 03_ensemble-classifiers.en.txt 4.9 KB
  1131. 02_loading-exploring-house-sale-data.en.txt 4.9 KB
  1132. 01_the-k-means-algorithm.en.txt 4.9 KB
  1133. 01_visualizing-the-ridge-cost.en.txt 4.9 KB
  1134. 04_computing-exploring-tf-idfs.en.txt 4.9 KB
  1135. 05_feature-engineering.en.srt 4.9 KB
  1136. 01_course-overview.en.srt 4.9 KB
  1137. 03_visualizing-the-lasso-cost-and-solution.en.txt 4.9 KB
  1138. 03_nn-search-with-kd-trees.en.txt 4.8 KB
  1139. 03_precision-recall-curve.en.txt 4.8 KB
  1140. 03_logistic-regression-model.en.srt 4.8 KB
  1141. 03_rewriting-the-model-for-all-observations-in-matrix-notation.en.srt 4.8 KB
  1142. 03_1-nearest-neighbor-algorithm.en.srt 4.8 KB
  1143. 03_creating-variables-in-python.en.txt 4.8 KB
  1144. 02_gibbs-sampling-from-10-000-feet.en.txt 4.8 KB
  1145. 07_examples-of-document-retrieval-in-action.en.srt 4.8 KB
  1146. 01_em-iterates-in-equations-and-pictures.en.txt 4.8 KB
  1147. 04_mapreduce-for-k-means.en.txt 4.8 KB
  1148. 01_why-gradient-ascent-won-t-scale.en.srt 4.8 KB
  1149. 03_what-you-ll-be-able-to-do.en.srt 1.5 KB
  1150. 04_other-examples-of-clustering.en.txt 4.8 KB
  1151. 01_review-of-gradient-ascent.en.txt 4.8 KB
  1152. 02_rewriting-the-single-observation-model-in-vector-notation.en.txt 4.8 KB
  1153. 01_feature-scaling-part-1.en.txt 4.8 KB
  1154. 03_an-unsupervised-task.en.txt 4.8 KB
  1155. 05_where-we-see-seasonality.en.srt 4.8 KB
  1156. 05_the-future-of-intelligent-applications.en.txt 2.2 KB
  1157. 02_mixed-membership-models-for-documents.en.srt 4.8 KB
  1158. 01_getting-started-with-python-jupyter-notebook-turi-create_Getting_started_with_Jupyter_Notebook.ipynb.zip 2.0 KB
  1159. 02_a-brief-recap.en.srt 4.7 KB
  1160. 03_module-2-recap.en.srt 4.7 KB
  1161. 03_review-of-overfitting-in-regression.en.srt 4.7 KB
  1162. 06_displaying-other-example-image-retrievals-with-a-python-lambda.en.srt 4.7 KB
  1163. 03_important-changes-from-previous-courses_instructions.html 2.7 KB
  1164. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Getting_started_with_Jupyter_Notebook.ipynb.zip 2.0 KB
  1165. 04_algorithm-refinement-improved-neural-network-architecture.en.srt 4.7 KB
  1166. 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.txt 4.7 KB
  1167. 01_implementing-ridge-regression-via-gradient-descent_REG04-NB02.ipynb.zip 4.7 KB
  1168. 03_the-lasso-objective-and-its-coefficient-path.en.txt 4.7 KB
  1169. 06_choosing-stepsize-and-convergence-criteria.en.txt 4.7 KB
  1170. 03_how-to-handle-the-intercept.en.txt 4.7 KB
  1171. 03_optional-tree-pruning-algorithm.en.srt 4.7 KB
  1172. 01_choosing-step-size.en.txt 4.7 KB
  1173. 05_outlining-the-first-half-of-the-course.en.txt 4.7 KB
  1174. 04_task-of-learning-decision-trees-from-data.en.srt 4.7 KB
  1175. 01_document-representation.en.txt 4.7 KB
  1176. 05_creating-functions-and-lambdas-in-python.en.txt 2.5 KB
  1177. 02_k-fold-cross-validation.en.txt 4.7 KB
  1178. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.3 KB
  1179. 01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.en.txt 4.7 KB
  1180. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_Turi_Getting_Started_with_SFrames.ipynb.zip 1.7 KB
  1181. 01_balancing-fit-and-magnitude-of-coefficients.en.txt 4.7 KB
  1182. 05_regularized-logistic-regression.en.txt 4.7 KB
  1183. 02_optional-pruning-decision-trees-to-avoid-overfitting.en.txt 4.7 KB
  1184. 06_the-ridge-coefficient-path.en.srt 4.6 KB
  1185. 04_finding-the-max-via-hill-climbing.en.txt 4.6 KB
  1186. 03_visualizing-decision-boundaries.en.txt 4.6 KB
  1187. 01_making-recommendations.en.txt 4.6 KB
  1188. 04_interacting-with-columns-of-an-sframe.en.txt 2.9 KB
  1189. 02_evaluating-a-classifier.en.srt 4.6 KB
  1190. 11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.txt 4.6 KB
  1191. 04_matrix-multiplication-code.en.txt 4.6 KB
  1192. 06_visualization-examples.en.txt 4.6 KB
  1193. 01_download-wiki-people-data_instructions.html 1.1 KB
  1194. 03_scaling-mixtures-of-gaussians-for-document-clustering.en.txt 4.6 KB
  1195. 06_running-gradient-descent.en.txt 4.6 KB
  1196. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1197. 01_precision-fraction-of-positive-predictions-that-are-actually-positive.en.txt 4.6 KB
  1198. 02_predicting-house-prices-a-case-study-in-regression.en.srt 2.0 KB
  1199. 02_predicting-house-prices-a-case-study-in-regression.en.txt 1.2 KB
  1200. 05_review-of-key-concepts.en.txt 4.5 KB
  1201. 02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.srt 4.5 KB
  1202. 03_overfitting-in-decision-trees.en.txt 4.5 KB
  1203. 04_decision-boundaries.en.srt 4.5 KB
  1204. 04_examples-of-classification-tasks.en.txt 4.5 KB
  1205. 04_the-dendrogram.en.txt 4.5 KB
  1206. 02_feature-split-selection-with-missing-data.en.txt 4.4 KB
  1207. 02_starting-a-jupyter-notebook.en.txt 4.4 KB
  1208. 03_all-subsets.en.txt 4.4 KB
  1209. 02_choosing-step-size.en.srt 4.4 KB
  1210. 03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.srt 4.4 KB
  1211. 01_what-is-collapsed-gibbs-sampling.en.srt 4.4 KB
  1212. 02_stochastic-gradient-learning-one-data-point-at-a-time.en.srt 4.4 KB
  1213. 02_approach-1-closed-form-solution.en.srt 4.4 KB
  1214. 04_learning-curves.en.txt 4.4 KB
  1215. 01_a-performance-metric-for-recommender-systems.en.txt 4.4 KB
  1216. 05_linear-classifiers.en.txt 4.4 KB
  1217. 03_adding-other-features.en.txt 2.3 KB
  1218. 05_goal-of-lda-inference.en.txt 4.4 KB
  1219. 08_exploring-other-features-of-the-data.en.txt 4.4 KB
  1220. 05_asymmetric-cost-functions.en.srt 4.4 KB
  1221. 01_penalizing-large-coefficients-to-mitigate-overfitting.en.txt 4.4 KB
  1222. 02_review-of-basics-of-probabilities.en.txt 4.4 KB
  1223. 05_using-precision-recall-to-compare-recommender-models.en.srt 4.4 KB
  1224. 03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.txt 4.4 KB
  1225. 03_normalizing-features.en.srt 4.3 KB
  1226. 02_defining-notation.en.srt 4.3 KB
  1227. 03_inference-making-predictions-forward-propagation.en.txt 4.3 KB
  1228. 01_the-need-for-bayesian-inference.en.txt 4.3 KB
  1229. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.5 KB
  1230. 03_checking-gradient-descent-for-convergence.en.txt 4.3 KB
  1231. 04_using-one-hot-encoding-of-categorical-features.en.txt 4.3 KB
  1232. 02_coordinate-descent.en.txt 4.3 KB
  1233. 03_splitting-the-data-into-training-and-test-sets.en.srt 2.9 KB
  1234. 03_splitting-the-data-into-training-and-test-sets.en.txt 1.7 KB
  1235. 04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.srt 4.3 KB
  1236. 02_whats-a-good-accuracy.en.srt 4.3 KB
  1237. 04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.en.txt 2.6 KB
  1238. 01_alternatives-to-the-sigmoid-activation.en.txt 4.3 KB
  1239. 05_evaluating-error-rmse-of-the-simple-model.en.srt 2.6 KB
  1240. 05_evaluating-error-rmse-of-the-simple-model.en.txt 1.5 KB
  1241. 03_rule-of-thumb-for-choosing-step-size.en.srt 4.3 KB
  1242. 01_predicting-class-probabilities-with-generalized-linear-models.en.txt 4.3 KB
  1243. 02_overfitting-in-boosting.en.txt 4.3 KB
  1244. 03_exploring-word-counts.en.txt 4.3 KB
  1245. 07_inspecting-the-model-coefficients-learned.en.srt 1.5 KB
  1246. 07_inspecting-the-model-coefficients-learned.en.txt 923 bytes
  1247. 02_finding-maxima-or-minima-analytically.en.txt 4.3 KB
  1248. 01_multiclass.en.srt 4.3 KB
  1249. 01_example-of-adaboost-in-action.en.txt 4.3 KB
  1250. 02_recall-fraction-of-positive-data-predicted-to-be-positive.en.srt 4.3 KB
  1251. 03_example-of-computing-derivative-for-logistic-regression.en.txt 4.3 KB
  1252. 09_learning-a-model-to-predict-house-prices-from-more-features.en.txt 2.1 KB
  1253. 05_finding-best-linear-classifier-with-gradient-ascent.en.srt 4.3 KB
  1254. 03_demo-of-deep-learning-model-on-imagenet-data.en.srt 4.3 KB
  1255. 02_state-action-value-function-example.en.txt 4.3 KB
  1256. 02_thresholding-ridge-coefficients.en.txt 4.3 KB
  1257. 01_polynomial-regression_REG03-NB01.ipynb.zip 4.2 KB
  1258. 05_linear-classifier-model.en.txt 4.2 KB
  1259. 02_simple-and-multiple-regression.en.txt 4.2 KB
  1260. 01_principle-of-occams-razor-learning-simpler-decision-trees.en.txt 4.2 KB
  1261. 01_ensemble-methods-impact-of-boosting-quick-recap.en.txt 4.2 KB
  1262. 02_interpreting-the-mixture-of-gaussian-terms.en.txt 4.2 KB
  1263. 01_collaborative-filtering-people-who-bought-this-also-bought.en.txt 4.2 KB
  1264. 02_predicting-house-prices_exam.html 4.2 KB
  1265. 05_creating-functions-and-lambdas-in-python.en.srt 4.2 KB
  1266. 05_bringing-it-all-together-featurized-matrix-factorization.en.srt 4.2 KB
  1267. 02_learning-algorithm-for-logistic-regression.en.srt 4.1 KB
  1268. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1269. 03_relationship-to-k-means.en.srt 4.1 KB
  1270. 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.txt 522 bytes
  1271. 02_weighted-error.en.txt 4.1 KB
  1272. 04_strategy-2-purification-by-imputing-missing-data.en.txt 4.1 KB
  1273. 02_optional-picking-the-best-threshold-to-split-on.en.srt 4.1 KB
  1274. 05_lasso-demo.en.txt 4.1 KB
  1275. 02_loading-image-data.en.srt 4.1 KB
  1276. 01_fitting-a-simple-linear-regression-model-on-housing-data_REG01-NB01.ipynb.zip 4.1 KB
  1277. 04_reweighing-data-to-focus-on-mistakes.en.txt 4.1 KB
  1278. 01_classification-ml-block-diagram.en.srt 4.1 KB
  1279. 01_what-we-didn-t-cover.en.srt 4.1 KB
  1280. 07_the-state-of-reinforcement-learning.en.srt 4.0 KB
  1281. 01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.txt 4.0 KB
  1282. 01__resources.html 4.0 KB
  1283. 01_the-matrix-completion-task.en.txt 4.0 KB
  1284. 04_overfitting-in-classification.en.txt 4.0 KB
  1285. 01_modifying-decision-trees-to-handle-missing-data.en.txt 4.0 KB
  1286. 06_optional-adding-regularization.en.srt 4.0 KB
  1287. 04_complexity-of-nn-search-with-kd-trees.en.txt 4.0 KB
  1288. 02_whats-a-good-accuracy.en.txt 2.7 KB
  1289. 02_visualizing-the-ridge-solution.en.txt 4.0 KB
  1290. 02_deep-learning-performance.en.srt 4.0 KB
  1291. 04_complexity-of-all-subsets.en.srt 4.0 KB
  1292. 03_why-do-we-need-activation-functions.en.txt 4.0 KB
  1293. 01_deep-learning-ml-block-diagram.en.srt 4.0 KB
  1294. 03_mapreduce-execution-overview-and-combiners.en.txt 4.0 KB
  1295. 01_adaboost-overview.en.srt 4.0 KB
  1296. 05_class-probabilities.en.srt 2.5 KB
  1297. 05_class-probabilities.en.txt 1.5 KB
  1298. 03_agglomerative-clustering.en.srt 4.0 KB
  1299. 03_timeline-of-scalable-machine-learning-stochastic-gradient.en.txt 4.0 KB
  1300. 01_classification-ml-block-diagram.en.txt 2.5 KB
  1301. 01_what-makes-the-lasso-objective-different.en.srt 4.0 KB
  1302. 02_divisive-clustering.en.txt 4.0 KB
  1303. 03_error-vs-amount-of-data.en.txt 4.0 KB
  1304. 01_a-brief-recap.en.srt 3.9 KB
  1305. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.0 KB
  1306. 04_interpreting-derivative-for-logistic-regression.en.txt 3.9 KB
  1307. 02_loading-exploring-product-review-data.en.txt 1.9 KB
  1308. 04_when-to-stop-recursing.en.txt 3.9 KB
  1309. 03_creating-the-word-count-vector.en.srt 2.2 KB
  1310. 02_simplified-cost-function-for-logistic-regression.en.txt 3.9 KB
  1311. 01_using-lasso-to-select-features_REG05-NB01.ipynb.zip 3.9 KB
  1312. 02_applications-of-machine-learning.en.txt 3.9 KB
  1313. 04_influence-of-high-leverage-points-removing-high-end-towns.en.srt 3.9 KB
  1314. 04_feature-selection-lasso-and-nearest-neighbor-regression.en.txt 3.9 KB
  1315. 02_learning-boosted-decision-stumps-with-adaboost.en.txt 3.9 KB
  1316. 05_defining-which-reviews-have-positive-or-negative-sentiment.en.txt 2.9 KB
  1317. 05_the-future-of-intelligent-applications.en.srt 3.9 KB
  1318. 01_motivating-the-use-of-multiple-inputs.en.txt 3.9 KB
  1319. 06_training-a-sentiment-classifier.en.txt 2.2 KB
  1320. 01_welcome-to-machine-learning.en.srt 3.9 KB
  1321. 02_lsh-as-an-alternative-to-kd-trees.en.txt 3.9 KB
  1322. 02_approach-1-closed-form-solution.en.txt 3.9 KB
  1323. 03_an-alternative-document-clustering-model.en.txt 3.9 KB
  1324. 04_interpreting-the-fitted-line.en.txt 3.9 KB
  1325. 04_approach-2-gradient-descent.en.txt 3.9 KB
  1326. 03_what-is-an-intelligent-restaurant-review-system.en.txt 3.9 KB
  1327. 06_jupyter-notebooks.en.txt 3.9 KB
  1328. 02_linear-classifiers-a-motivating-example.en.srt 3.9 KB
  1329. 03_training-evaluating-a-classifier-using-raw-image-pixels.en.txt 3.9 KB
  1330. 05_tensorflow-implementation-of-content-based-filtering.en.txt 3.9 KB
  1331. 04_building-a-recommender-system-via-classification.en.txt 3.9 KB
  1332. 02_predicting-loan-defaults-with-decision-trees.en.txt 3.9 KB
  1333. 02_recommendations-from-known-user-item-features.en.txt 3.8 KB
  1334. 01_defining-our-least-squares-optimization-objective.en.srt 3.8 KB
  1335. 03_what-do-we-mean-by-loss.en.txt 3.8 KB
  1336. 02_assessing-convergence-and-other-lasso-solvers.en.srt 3.8 KB
  1337. 01_forward-prop-in-a-single-layer.en.txt 3.8 KB
  1338. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1339. 02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.en.srt 858 bytes
  1340. 02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.en.txt 528 bytes
  1341. 02_effect-of-popular-items.en.srt 3.8 KB
  1342. 02_optional-responsibilities-as-bayes-rule.en.txt 3.8 KB
  1343. 03_what-is-the-document-retrieval-task.en.txt 1.3 KB
  1344. 06_training-a-sentiment-classifier.en.srt 3.8 KB
  1345. 01_encoding-categorical-inputs.en.txt 3.8 KB
  1346. 03_predictions-in-matrix-form.en.srt 3.8 KB
  1347. 02_the-resulting-ridge-objective-and-its-extreme-solutions.en.txt 3.8 KB
  1348. 03_adding-other-features.en.srt 3.8 KB
  1349. 04_univariate-gaussian-distributions.en.srt 3.8 KB
  1350. 07_assumed-background.en.txt 3.8 KB
  1351. 05_using-apply-for-data-transformation.en.txt 3.8 KB
  1352. 02_very-optional-expressing-the-log-likelihood.en.srt 3.7 KB
  1353. 04_creating-evaluating-a-personalized-song-recommender.en.txt 3.7 KB
  1354. 03_discussing-the-closed-form-solution.en.txt 3.7 KB
  1355. 07_retrieving-similar-documents-using-nearest-neighbor-search.en.txt 2.2 KB
  1356. 04_making-decisions-policies-in-reinforcement-learning.en.srt 3.7 KB
  1357. 02_trading-off-precision-and-recall.en.txt 3.7 KB
  1358. 01_clustering-documents-task-overview.en.txt 2.1 KB
  1359. 03_test-error-what-we-can-actually-compute.en.txt 3.7 KB
  1360. 06_complexity-of-the-greedy-forward-stepwise-algorithm.en.srt 3.7 KB
  1361. 02_the-boosting-question.en.txt 3.7 KB
  1362. 01_gradients-derivatives-in-multiple-dimensions.en.txt 3.7 KB
  1363. 05_finding-the-min-via-hill-descent.en.srt 3.7 KB
  1364. 01_why-hierarchical-clustering.en.srt 3.7 KB
  1365. 02_gradient-descent-multidimensional-hill-descent.en.txt 3.7 KB
  1366. 02_distance-metrics.en.txt 3.7 KB
  1367. 06_calculating-tf-idf-vectors.en.txt 3.7 KB
  1368. 02_l2-regularized-logistic-regression.en.txt 3.7 KB
  1369. 01_recursive-greedy-algorithm.en.txt 3.7 KB
  1370. 06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.srt 3.7 KB
  1371. 01_training-and-evaluating-a-classifier.en.txt 3.7 KB
  1372. 01_precision-recall-extremes.en.srt 3.6 KB
  1373. 03_1-nn-algorithm.en.srt 3.6 KB
  1374. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.0 KB
  1375. 03_intuition-behind-maximum-likelihood-estimation.en.txt 3.6 KB
  1376. 02_using-stochastic-gradient-for-online-learning.en.txt 3.6 KB
  1377. 09_learning-a-model-to-predict-house-prices-from-more-features.en.srt 3.6 KB
  1378. 01_computing-the-gradient-of-the-ridge-objective.en.txt 3.6 KB
  1379. 02_loading-and-exploring-song-data.en.txt 3.6 KB
  1380. 05_classification-with-multiple-outputs-optional.en.txt 3.6 KB
  1381. 02_influence-of-high-leverage-points-exploring-the-data.en.txt 3.6 KB
  1382. 03_weighted-k-nearest-neighbors.en.txt 3.6 KB
  1383. 05_regression-ml-block-diagram.en.txt 3.6 KB
  1384. 01_deciding-what-to-try-next.en.txt 3.6 KB
  1385. 04_using-the-output-of-collapsed-gibbs-sampling.en.txt 3.6 KB
  1386. 05_adding-higher-order-effects.en.txt 3.6 KB
  1387. 01_coordinate-descent-for-lasso-normalized-features.en.txt 3.5 KB
  1388. 03_comparing-gradient-to-stochastic-gradient.en.txt 3.5 KB
  1389. 06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.en.txt 2.2 KB
  1390. 02_challenge-of-missing-data.en.txt 3.5 KB
  1391. 02_the-feature-selection-task.en.txt 3.5 KB
  1392. 07_examples-of-document-retrieval-in-action.en.txt 2.8 KB
  1393. 02_visualizing-overconfident-predictions.en.txt 3.5 KB
  1394. 02_who-is-this-specialization-for.en.txt 3.5 KB
  1395. 02_case-study-where-accuracy-is-not-best-metric-for-classification.en.txt 3.5 KB
  1396. 07_retrieving-similar-documents-using-nearest-neighbor-search.en.srt 3.5 KB
  1397. 03_maximizing-a-1d-function-a-worked-example.en.srt 3.5 KB
  1398. 02_a-review-of-overfitting.en.srt 3.5 KB
  1399. 01_the-boosting-theorem.en.txt 3.5 KB
  1400. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1401. 03_what-is-a-visual-product-recommender.en.txt 3.5 KB
  1402. 02_recommender-systems-overview.en.srt 2.0 KB
  1403. 02_recommender-systems-overview.en.txt 1.3 KB
  1404. 02_clustering-documents-an-unsupervised-learning-task.en.txt 3.4 KB
  1405. 01_exploring-different-multiple-regression-models-for-house-price-prediction_REG02-NB01.ipynb.zip 3.4 KB
  1406. 04_hope-for-unsupervised-learning-and-some-challenge-cases.en.txt 3.4 KB
  1407. 05_visualizing-scaling-behavior-of-kd-trees.en.txt 3.4 KB
  1408. 09_exploring-the-most-positive-negative-aspects-of-a-product.en.txt 3.4 KB
  1409. 05_optional-measuring-convergence.en.txt 3.4 KB
  1410. 04_what-is-the-course-about.en.txt 3.4 KB
  1411. 02_sampling-with-replacement.en.txt 3.4 KB
  1412. 04_assumed-background.en.txt 3.4 KB
  1413. 07_evaluating-a-classifier-the-roc-curve.en.txt 3.4 KB
  1414. 05_machine-learning-is-changing-the-world.en.txt 3.4 KB
  1415. 02_effect-of-popular-items.en.txt 2.3 KB
  1416. 02_sframes_exam.html 3.4 KB
  1417. 01_clustering-documents-task-overview.en.srt 3.4 KB
  1418. 01_using-multiple-decision-trees.en.txt 3.4 KB
  1419. 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.txt 3.4 KB
  1420. 03_what-is-good-performance-for-a-classifier.en.txt 3.4 KB
  1421. 06_decision-boundaries.en.txt 3.4 KB
  1422. 03_polynomial-regression.en.txt 3.3 KB
  1423. 02_the-general-mapreduce-abstraction.en.txt 3.3 KB
  1424. 01_how-we-got-into-ml.en.txt 3.3 KB
  1425. 05_computing-distances-between-wikipedia-articles.en.txt 3.3 KB
  1426. 04_course-overview.en.txt 3.3 KB
  1427. 03_predictions-in-matrix-form.en.txt 2.3 KB
  1428. 03_deploying-an-ml-service.en.txt 3.3 KB
  1429. 02_training-test-curves.en.txt 3.3 KB
  1430. 05_challenges-of-deep-learning.en.srt 3.3 KB
  1431. 03_strategy-1-purification-by-skipping-missing-data.en.txt 3.3 KB
  1432. 02_recommending-songs_exam.html 3.3 KB
  1433. 05_bringing-it-all-together-featurized-matrix-factorization.en.txt 2.6 KB
  1434. 04_exploring-the-most-popular-product.en.txt 3.3 KB
  1435. 01_can-we-use-regularization-for-feature-selection.en.txt 3.3 KB
  1436. 04_using-probabilities-in-classification.en.srt 3.3 KB
  1437. 02_the-goal-of-clustering.en.txt 3.3 KB
  1438. 02_optimal-recommenders.en.srt 2.7 KB
  1439. 02_optimal-recommenders.en.txt 1.5 KB
  1440. 01_limitations-of-kd-trees.en.txt 3.3 KB
  1441. 02_limitations-of-parametric-regression.en.txt 3.3 KB
  1442. 03_discussing-the-closed-form-solution.en.txt 3.3 KB
  1443. 04_querying-the-nearest-neighbors-model-to-retrieve-images.en.txt 3.3 KB
  1444. 04_components-of-latent-dirichlet-allocation-model.en.srt 3.3 KB
  1445. 02_loading-exploring-product-review-data.en.srt 3.3 KB
  1446. 06_lsh-in-higher-dimensions.en.txt 3.3 KB
  1447. 03_creating-evaluating-a-popularity-based-song-recommender.en.txt 3.3 KB
  1448. 03_smart-initialization-via-k-means.en.txt 3.2 KB
  1449. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.0 KB
  1450. 01_selecting-tuning-parameters-via-cross-validation.en.txt 3.2 KB
  1451. 10_applying-learned-models-to-predict-price-of-an-average-house.en.txt 3.2 KB
  1452. 02_careful-with-step-sizes-that-are-too-large.en.txt 3.2 KB
  1453. 06_visualizing-predictions-of-simple-model-with-matplotlib.en.txt 3.2 KB
  1454. 04_other-regression-examples.en.txt 3.2 KB
  1455. 02_the-sigmoid-or-logistic-link-function.en.txt 3.2 KB
  1456. 02_loading-image-data.en.srt 3.2 KB
  1457. 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.txt 3.2 KB
  1458. 02_learning-a-decision-stump.en.txt 3.2 KB
  1459. 02_starting-turi-create-loading-an-sframe.en.txt 3.2 KB
  1460. 02_approach-1-closed-form-solution.en.txt 3.2 KB
  1461. 05_using-precision-recall-to-compare-recommender-models.en.txt 2.6 KB
  1462. 01_how-neural-networks-are-implemented-efficiently.en.txt 3.2 KB
  1463. 03_canvas-for-data-visualization.en.txt 3.2 KB
  1464. 05_prioritizing-important-words-with-tf-idf.en.txt 3.2 KB
  1465. 05_overfitting-for-more-general-multiple-regression-models.en.txt 3.2 KB
  1466. 05_visualizing-overfitting-with-high-degree-polynomial-features.en.txt 3.2 KB
  1467. 05_overview-of-learning-logistic-regression-models.en.srt 3.2 KB
  1468. 03_intuition-behind-linear-classifiers.en.txt 3.2 KB
  1469. 06_algorithmic-summary-of-gradient-descent-approach.en.txt 3.2 KB
  1470. 01_what-is-clustering.en.txt 3.2 KB
  1471. 01_the-online-learning-task.en.txt 3.1 KB
  1472. 04_regression-fundamentals-the-task.en.srt 3.1 KB
  1473. 02_loading-exploring-wikipedia-data.en.txt 3.1 KB
  1474. 05_unsupervised-learning-part-2.en.txt 3.1 KB
  1475. 03_what-is-the-goal-and-how-might-you-naively-address-it.en.txt 3.1 KB
  1476. 01_tensorflow-implementation.en.txt 3.1 KB
  1477. 06_regression-with-general-features-of-1-input.en.srt 3.1 KB
  1478. 02_searching-for-images-a-case-study-in-deep-learning.en.srt 550 bytes
  1479. 02_searching-for-images-a-case-study-in-deep-learning.en.txt 320 bytes
  1480. 04_optional-learning-from-batches-of-data.en.txt 3.1 KB
  1481. 05_where-we-see-seasonality.en.txt 3.1 KB
  1482. 08_applying-model-to-find-most-positive-negative-reviews-for-a-product.en.txt 3.1 KB
  1483. 03_regression-with-features-of-multiple-inputs.en.txt 3.1 KB
  1484. 01_why-would-stochastic-gradient-ever-work.en.txt 3.1 KB
  1485. 03_module-2-recap.en.txt 3.0 KB
  1486. 08_a-big-week-ahead_instructions.html 1.2 KB
  1487. 01_course-overview.en.txt 3.0 KB
  1488. 02_mixed-membership-models-for-documents.en.txt 3.0 KB
  1489. 02_symptoms-of-overfitting-in-polynomial-regression.en.srt 3.0 KB
  1490. 03_k-means-a-clustering-algorithm.en.txt 3.0 KB
  1491. 02_deep-learning-performance.en.txt 2.5 KB
  1492. 02_a-worked-example-for-lda-initial-setup.en.txt 3.0 KB
  1493. 03_optional-tree-pruning-algorithm.en.txt 3.0 KB
  1494. 03_demo-of-deep-learning-model-on-imagenet-data.en.txt 2.7 KB
  1495. 03_1-nearest-neighbor-algorithm.en.txt 3.0 KB
  1496. 04_other-examples-of-deep-learning-in-computer-vision.en.srt 2.0 KB
  1497. 04_other-examples-of-deep-learning-in-computer-vision.en.txt 1.2 KB
  1498. 02_a-brief-recap.en.txt 3.0 KB
  1499. 01_numpy-tutorial_numpy-tutorial.ipynb.zip 3.0 KB
  1500. 05_challenges-of-deep-learning.en.txt 2.1 KB
  1501. 03_rewriting-the-model-for-all-observations-in-matrix-notation.en.txt 2.9 KB
  1502. 02_implementing-gradient-descent-for-multiple-regression_numpy-tutorial-py3.ipynb.zip.ipynb 2.9 KB
  1503. 01_polynomial-regression_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1504. 01_observing-effects-of-l2-penalty-in-polynomial-regression_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1505. 01_implementing-ridge-regression-via-gradient-descent_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1506. 01_deep-learning-ml-block-diagram.en.txt 2.4 KB
  1507. 01_using-lasso-to-select-features_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1508. 01_implementing-lasso-using-coordinate-descent_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1509. 01_predicting-house-prices-using-k-nearest-neighbors-regression_numpy-tutorial-py3.ipynb.zip 2.9 KB
  1510. 06_the-ridge-coefficient-path.en.txt 2.9 KB
  1511. 01_welcome.en.txt 2.9 KB
  1512. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.2 KB
  1513. 03_welcome.en.srt 2.9 KB
  1514. 02_loading-image-data.en.txt 2.4 KB
  1515. 02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.en.txt 2.9 KB
  1516. 03_normalizing-features.en.txt 2.8 KB
  1517. 01_summary-and-thank-you.en.txt 2.8 KB
  1518. 06_displaying-other-example-image-retrievals-with-a-python-lambda.en.txt 2.7 KB
  1519. 02_defining-notation.en.txt 2.7 KB
  1520. 05_asymmetric-cost-functions.en.txt 2.6 KB
  1521. 01_the-simple-linear-regression-model.en.srt 2.6 KB
  1522. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_FND06-NB02.ipynb.zip 1.9 KB
  1523. 02_approach-1-closed-form-solution.en.txt 2.6 KB
  1524. 07_the-state-of-reinforcement-learning.en.txt 2.6 KB
  1525. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_instructions.html 2.4 KB
  1526. 04_defining-overfitting.en.srt 2.6 KB
  1527. 02_loading-image-data.en.txt 1.9 KB
  1528. 03_k-nn-for-classification.en.srt 2.6 KB
  1529. 03_creating-a-nearest-neighbors-model-for-image-retrieval.en.srt 1.7 KB
  1530. 03_creating-a-nearest-neighbors-model-for-image-retrieval.en.txt 1.0 KB
  1531. 05_training-test-split.en.srt 2.5 KB
  1532. 04_influence-of-high-leverage-points-removing-high-end-towns.en.txt 2.5 KB
  1533. 04_algorithm-refinement-improved-neural-network-architecture.en.txt 2.5 KB
  1534. 01_defining-our-least-squares-optimization-objective.en.txt 2.5 KB
  1535. 04_complexity-of-all-subsets.en.txt 2.5 KB
  1536. 05_querying-for-the-most-similar-images-for-car-image.en.txt 1.2 KB
  1537. 02_assessing-convergence-and-other-lasso-solvers.en.txt 2.4 KB
  1538. 01_what-makes-the-lasso-objective-different.en.txt 2.4 KB
  1539. 03_coordinate-descent-for-lasso-unnormalized-features.en.srt 2.4 KB
  1540. 02_thank-you.en.srt 2.4 KB
  1541. 04_a-brief-recap.en.srt 2.3 KB
  1542. 04_thank-you.en.srt 2.3 KB
  1543. 02_multiclass-classification-with-decision-trees.en.txt 2.3 KB
  1544. 06_complexity-of-the-greedy-forward-stepwise-algorithm.en.txt 2.3 KB
  1545. 01_computing-the-gradient-of-rss.en.txt 2.2 KB
  1546. 01_numpy-tutorial_instructions.html 2.2 KB
  1547. 05_finding-the-min-via-hill-descent.en.txt 2.2 KB
  1548. 06_comparing-the-approaches.en.srt 2.1 KB
  1549. 01_download-notebooks-to-follow-along_Philadelphia_Crime_Rate_noNA.csv.zip 2.1 KB
  1550. 04_approach-2-gradient-descent.en.srt 2.1 KB
  1551. 04_a-brief-recap.en.srt 2.0 KB
  1552. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1553. 03_maximizing-a-1d-function-a-worked-example.en.txt 2.0 KB
  1554. 02_you-ve-made-it.en.txt 636 bytes
  1555. 04_making-decisions-policies-in-reinforcement-learning.en.txt 2.0 KB
  1556. 03_how-ridge-regression-balances-bias-and-variance.en.srt 2.0 KB
  1557. 02_a-brief-recap.en.srt 2.0 KB
  1558. 06_regression-with-general-features-of-1-input.en.txt 1.9 KB
  1559. 04_regression-fundamentals-the-task.en.txt 1.9 KB
  1560. 02_symptoms-of-overfitting-in-polynomial-regression.en.txt 1.9 KB
  1561. 03_welcome.en.txt 1.8 KB
  1562. 01_download-notebooks-to-follow-along_instructions.html 1.8 KB
  1563. 01_important-update-regarding-the-machine-learning-specialization_instructions.html 1.8 KB
  1564. 02_optional-opportunity-to-mentor-other-learners_instructions.html 1.7 KB
  1565. 04_download-the-notebook-and-follow-along_instructions.html 1.6 KB
  1566. 01_the-simple-linear-regression-model.en.txt 1.6 KB
  1567. 03_k-nn-for-classification.en.txt 1.6 KB
  1568. 04_download-the-notebook-and-follow-along_instructions.html 1.6 KB
  1569. 05_training-test-split.en.txt 1.6 KB
  1570. 01_a-brief-recap.en.srt 1.6 KB
  1571. 03_coordinate-descent-for-lasso-unnormalized-features.en.txt 1.5 KB
  1572. 04_thank-you.en.txt 1.4 KB
  1573. 02_a-case-study-in-predicting-house-prices.en.srt 1.5 KB
  1574. 04_defining-overfitting.en.txt 1.5 KB
  1575. 03_download-the-notebook-and-follow-along_instructions.html 1.4 KB
  1576. 06_comparing-the-approaches.en.txt 1.3 KB
  1577. 02_slides-presented-in-this-module_instructions.html 1.1 KB
  1578. 03_how-ridge-regression-balances-bias-and-variance.en.txt 1.3 KB
  1579. 04_a-brief-recap.en.txt 1.3 KB
  1580. 04_approach-2-gradient-descent.en.txt 1.3 KB
  1581. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1582. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1583. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1584. 01_slides-presented-in-this-module_instructions.html 1.2 KB
  1585. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1586. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1587. 02_a-brief-recap.en.txt 1.1 KB
  1588. 01_slides-presented-in-this-module_instructions.html 1.1 KB
  1589. 01_optional-reading-review-of-matrix-algebra_instructions.html 1.1 KB
  1590. 01_a-brief-recap.en.txt 1.0 KB
  1591. 06_a-brief-recap.en.txt 1022 bytes
  1592. 02_multiple-regression-intro.en.txt 380 bytes
  1593. 54 62.4 KB
  1594. 06_hidden-markov-models.mp4 32.1 MB
  1595. 55 1.9 MB
  1596. 03_learning-the-state-value-function.mp4 31.1 MB
  1597. 56 881.3 KB
  1598. 03_collaborative-filtering-algorithm.mp4 31.0 MB
  1599. 57 998.0 KB
  1600. 01_what-is-reinforcement-learning.mp4 31.0 MB
  1601. 58 1.0 MB
  1602. 06_choosing-what-features-to-use.mp4 30.9 MB
  1603. 59 1.1 MB
  1604. 04_modeling-seasonality.mp4 30.0 MB
  1605. 60 2.0 MB
  1606. 02_computation-graph-optional.mp4 30.0 MB
  1607. 61 32.7 KB
  1608. 04_what-happens-after-deployment.mp4 30.0 MB
  1609. 62 47.8 KB
  1610. 02_convergence-initialization-and-overfitting-of-em.mp4 29.9 MB
  1611. 63 95.6 KB
  1612. 03_model-selection-and-training-cross-validation-test-sets.mp4 29.6 MB
  1613. 64 386.2 KB
  1614. 04_cost-function-intuition.mp4 29.6 MB
  1615. 65 448.3 KB
  1616. 04_optimization-objective.mp4 29.5 MB
  1617. 66 505.1 KB
  1618. 02_module-1-recap.mp4 29.3 MB
  1619. 67 702.5 KB
  1620. 03_a-standard-gibbs-sampler-for-lda.mp4 29.3 MB
  1621. 68 752.4 KB
  1622. 06_why-a-case-study-approach.mp4 29.1 MB
  1623. 69 962.5 KB
  1624. 02_learning-process.mp4 29.0 MB
  1625. 70 992.7 KB
  1626. 03_the-return-in-reinforcement-learning.mp4 29.0 MB
  1627. 71 1016.0 KB
  1628. 05_module-by-module-topics-covered.mp4 29.0 MB
  1629. 72 1.0 MB
  1630. 01_predicting-sentiment-from-product-reviews_amazon_baby.csv.zip 28.7 MB
  1631. 73 1.3 MB
  1632. 01_exploring-precision-and-recall_amazon_baby.csv.zip 28.7 MB
  1633. 74 1.3 MB
  1634. 04_who-we-are.mp4 28.4 MB
  1635. 75 1.6 MB
  1636. 01_is-there-a-path-to-agi.mp4 28.1 MB
  1637. 76 1.9 MB
  1638. 05_deciding-what-to-try-next-revisited.mp4 28.0 MB
  1639. 77 2.0 MB
  1640. 02_pca-algorithm-optional.mp4 28.0 MB
  1641. 78 2.0 MB
  1642. 07_specialization-overview.mp4 27.3 MB
  1643. 79 734.4 KB
  1644. 01_example-of-continuous-state-space-applications.mp4 27.0 MB
  1645. 80 974.5 KB
  1646. 06_bias-variance-and-neural-networks.mp4 26.9 MB
  1647. 81 1.1 MB
  1648. 02_neurons-and-the-brain.mp4 26.9 MB
  1649. 82 1.1 MB
  1650. 03_where-we-see-recommender-systems-in-action.mp4 26.7 MB
  1651. 83 1.3 MB
  1652. 01_reducing-the-number-of-features-optional.mp4 26.7 MB
  1653. 84 1.3 MB
  1654. 03_bellman-equation.mp4 26.7 MB
  1655. 85 1.3 MB
  1656. 01_finding-unusual-events.mp4 26.3 MB
  1657. 86 1.7 MB
  1658. 03_larger-neural-network-example-optional.mp4 26.1 MB
  1659. 87 1.9 MB
  1660. 01_slides-presented-in-this-module_closing-annotated.pdf 26.1 MB
  1661. 88 1.9 MB
  1662. 01_what-is-machine-learning.mp4 26.0 MB
  1663. 89 16.3 KB
  1664. 05_module-4-recap.mp4 25.9 MB
  1665. 90 57.6 KB
  1666. 06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4 25.6 MB
  1667. 91 457.7 KB
  1668. 01_training-validation-test-split-for-model-selection-fitting-and-assessment.mp4 25.4 MB
  1669. 92 646.0 KB
  1670. 06_fairness-bias-and-ethics.mp4 25.3 MB
  1671. 93 667.5 KB
  1672. 01_deep-features-for-image-retrieval-assignment_image_train_data.zip 25.3 MB
  1673. 94 668.1 KB
  1674. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip 25.3 MB
  1675. 95 668.1 KB
  1676. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_image_train_data.zip 25.3 MB
  1677. 96 668.1 KB
  1678. 05_algorithm-refinement-greedy-policy.mp4 25.3 MB
  1679. 97 749.5 KB
  1680. 05_ridge-regression-demo.mp4 24.9 MB
  1681. 98 1.1 MB
  1682. 02_data-in-tensorflow.mp4 24.8 MB
  1683. 99 1.2 MB
  1684. 04_ethical-use-of-recommender-systems.mp4 24.8 MB
  1685. 100 1.2 MB
  1686. 04_interpreting-the-multiple-regression-fit.mp4 24.7 MB
  1687. 101 1.3 MB
  1688. 01_cost-function-for-logistic-regression.mp4 24.6 MB
  1689. 102 1.4 MB
  1690. 03_building-a-neural-network.mp4 24.4 MB
  1691. 103 1.6 MB
  1692. 06_deep-features.mp4 24.4 MB
  1693. 104 1.6 MB
  1694. 02_deep-learning-for-content-based-filtering.mp4 24.3 MB
  1695. 105 1.7 MB
  1696. 03_demand-prediction.mp4 24.2 MB
  1697. 106 1.8 MB
  1698. 02_training-details.mp4 24.1 MB
  1699. 107 1.9 MB
  1700. 01_the-problem-of-overfitting.mp4 24.0 MB
  1701. 108 34.4 KB
  1702. 04_developing-and-evaluating-an-anomaly-detection-system.mp4 23.9 MB
  1703. 109 100.1 KB
  1704. 02_choosing-a-split-information-gain.mp4 23.8 MB
  1705. 110 235.6 KB
  1706. 02_using-per-item-features.mp4 23.5 MB
  1707. 111 522.5 KB
  1708. 02_choosing-activation-functions.mp4 23.4 MB
  1709. 112 628.6 KB
  1710. 04_learning-curves.mp4 23.3 MB
  1711. 113 738.3 KB
  1712. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_word_count.npz.zip 23.2 MB
  1713. 114 831.8 KB
  1714. 04_examples-of-classification-tasks.mp4 23.0 MB
  1715. 115 1003.7 KB
  1716. 06_approximate-k-nn-search-using-kd-trees.mp4 22.9 MB
  1717. 116 1.1 MB
  1718. 06_polynomial-regression.mp4 22.8 MB
  1719. 117 1.2 MB
  1720. 02_multiclass-classification-with-1-versus-all.mp4 22.7 MB
  1721. 118 1.3 MB
  1722. 02_kd-tree-representation.mp4 22.6 MB
  1723. 119 1.4 MB
  1724. 01_gradient-descent.mp4 22.5 MB
  1725. 120 1.5 MB
  1726. 11_applying-learned-models-to-predict-price-of-two-fancy-houses.mp4 22.4 MB
  1727. 121 1.6 MB
  1728. 03_regression-fundamentals-data-model.mp4 22.4 MB
  1729. 122 1.6 MB
  1730. 02_motiving-probabilistic-clustering-models.mp4 22.3 MB
  1731. 123 1.7 MB
  1732. 04_word-count-representation-for-measuring-similarity.mp4 22.3 MB
  1733. 124 1.7 MB
  1734. 04_learning-curves.mp4 22.2 MB
  1735. 125 1.8 MB
  1736. 02_early-stopping-in-learning-decision-trees.mp4 22.2 MB
  1737. 126 1.8 MB
  1738. 03_whats-ahead-in-the-specialization.mp4 22.2 MB
  1739. 127 1.8 MB
  1740. 01_welcome-to-machine-learning.mp4 22.2 MB
  1741. 128 1.8 MB
  1742. 02_trading-off-precision-and-recall.mp4 22.2 MB
  1743. 129 1.8 MB
  1744. 04_the-capstone-and-an-example-intelligent-application.mp4 22.2 MB
  1745. 130 1.8 MB
  1746. 04_approach-2-gradient-descent.mp4 22.1 MB
  1747. 131 1.9 MB
  1748. 04_what-is-this-course-about.mp4 22.1 MB
  1749. 132 1.9 MB
  1750. 05_searching-neighboring-bins.mp4 22.0 MB
  1751. 133 2.0 MB
  1752. 01_performance-of-nn-as-amount-of-data-grows.mp4 21.9 MB
  1753. 134 133.4 KB
  1754. 01_computing-soft-assignments-from-known-cluster-parameters.mp4 21.8 MB
  1755. 135 242.5 KB
  1756. 02_generalization-error-what-we-really-want.mp4 21.8 MB
  1757. 136 245.9 KB
  1758. 03_optional-another-perspecting-on-overfitting-in-logistic-regression.mp4 21.7 MB
  1759. 137 326.0 KB
  1760. 01_optional-motivating-pruning.mp4 21.6 MB
  1761. 138 378.8 KB
  1762. 05_to-normalize-or-not-and-other-distance-considerations.mp4 21.6 MB
  1763. 139 428.0 KB
  1764. 03_aggregating-over-unknown-classes-in-an-image-dataset.mp4 21.6 MB
  1765. 140 450.1 KB
  1766. 01_other-applications-of-clustering.mp4 21.5 MB
  1767. 141 461.8 KB
  1768. 01_slides-presented-in-this-module_regression-intro-annotated.pdf 21.5 MB
  1769. 142 486.3 KB
  1770. 02_logistic-regression.mp4 21.5 MB
  1771. 143 528.2 KB
  1772. 01_1-nearest-neighbor-regression-approach.mp4 21.5 MB
  1773. 144 537.4 KB
  1774. 04_boosting.mp4 21.4 MB
  1775. 145 593.7 KB
  1776. 04_estimating-cluster-parameters-from-soft-assignments.mp4 21.4 MB
  1777. 146 614.2 KB
  1778. 02_general-implementation-of-forward-propagation.mp4 21.3 MB
  1779. 147 685.4 KB
  1780. 05_bivariate-and-multivariate-gaussians.mp4 21.3 MB
  1781. 148 756.9 KB
  1782. 04_computing-the-cost-of-a-d-dimensional-curve.mp4 21.2 MB
  1783. 149 775.5 KB
  1784. 04_assessing-the-quality-and-choosing-the-number-of-clusters.mp4 21.2 MB
  1785. 150 838.2 KB
  1786. 02_regularization-and-bias-variance.mp4 21.1 MB
  1787. 151 907.3 KB
  1788. 04_xgboost.mp4 21.1 MB
  1789. 152 944.6 KB
  1790. 02_distance-metrics-euclidean-and-scaled-euclidean.mp4 21.1 MB
  1791. 153 972.1 KB
  1792. 01_mixture-of-gaussians.mp4 21.0 MB
  1793. 154 978.7 KB
  1794. 01_motivations.mp4 21.0 MB
  1795. 155 1.0 MB
  1796. 02_implementing-gradient-descent.mp4 20.9 MB
  1797. 156 1.1 MB
  1798. 05_regularized-logistic-regression.mp4 20.9 MB
  1799. 157 1.1 MB
  1800. 01_irreducible-error-and-bias.mp4 20.9 MB
  1801. 158 1.1 MB
  1802. 02_gaussian-normal-distribution.mp4 20.9 MB
  1803. 159 1.1 MB
  1804. 04_coordinate-descent-for-least-squares-regression-normalized-features.mp4 20.8 MB
  1805. 160 1.2 MB
  1806. 02_slides-presented-in-this-module_intro.pdf 20.8 MB
  1807. 161 1.2 MB
  1808. 01_sparse-logistic-regression-with-l1-regularization.mp4 20.8 MB
  1809. 162 1.2 MB
  1810. 03_assessing-performance-and-ridge-regression.mp4 20.7 MB
  1811. 163 1.3 MB
  1812. 02_softmax.mp4 20.7 MB
  1813. 164 1.3 MB
  1814. 01_making-recommendations.mp4 20.4 MB
  1815. 165 1.6 MB
  1816. 01_neural-network-layer.mp4 20.4 MB
  1817. 166 1.6 MB
  1818. 01_motivating-mapreduce.mp4 20.4 MB
  1819. 167 1.6 MB
  1820. 01_training-error-assessing-loss-on-the-training-set.mp4 20.4 MB
  1821. 168 1.6 MB
  1822. 03_anomaly-detection-algorithm.mp4 20.3 MB
  1823. 169 1.7 MB
  1824. 05_anomaly-detection-vs-supervised-learning.mp4 20.3 MB
  1825. 170 1.7 MB
  1826. 01_diagnosing-bias-and-variance.mp4 20.3 MB
  1827. 171 1.7 MB
  1828. 01_linear-regression-model-part-1.mp4 20.3 MB
  1829. 172 1.7 MB
  1830. 05_agglomerative-clustering-details.mp4 20.2 MB
  1831. 173 1.8 MB
  1832. 03_outline-of-second-half-of-course.mp4 20.2 MB
  1833. 174 1.8 MB
  1834. 04_conditional-statements-and-loops-in-python.mp4 20.1 MB
  1835. 175 1.9 MB
  1836. 04_distance-metrics-cosine-similarity.mp4 20.1 MB
  1837. 176 1.9 MB
  1838. 02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.mp4 20.1 MB
  1839. 177 1.9 MB
  1840. 01_collaborative-filtering-vs-content-based-filtering.mp4 20.0 MB
  1841. 178 30.2 KB
  1842. 03_review-of-basics-of-conditional-probabilities.mp4 19.9 MB
  1843. 179 93.5 KB
  1844. 06_jupyter-notebooks.mp4 19.9 MB
  1845. 180 106.2 KB
  1846. 01_predicting-class-probabilities-with-generalized-linear-models.mp4 19.9 MB
  1847. 181 129.6 KB
  1848. 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.mp4 19.8 MB
  1849. 182 161.4 KB
  1850. 01_state-action-value-function-definition.mp4 19.8 MB
  1851. 183 163.9 KB
  1852. 04_binary-labels-favs-likes-and-clicks.mp4 19.8 MB
  1853. 184 167.3 KB
  1854. 04_regularized-linear-regression.mp4 19.8 MB
  1855. 185 198.2 KB
  1856. 04_other-examples-of-clustering.mp4 19.8 MB
  1857. 186 230.5 KB
  1858. 03_k-means-algorithm.mp4 19.8 MB
  1859. 187 244.4 KB
  1860. 04_module-3-recap.mp4 19.7 MB
  1861. 188 290.5 KB
  1862. 02_additional-layer-types.mp4 19.5 MB
  1863. 189 479.0 KB
  1864. 03_influence-of-high-leverage-points-removing-center-city.mp4 19.5 MB
  1865. 190 494.8 KB
  1866. 01_slides-presented-in-this-module_closing.pdf 19.5 MB
  1867. 191 537.7 KB
  1868. 03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.mp4 19.5 MB
  1869. 192 548.8 KB
  1870. 02_evaluating-a-model.mp4 19.5 MB
  1871. 193 563.1 KB
  1872. 06_assumed-background.mp4 19.4 MB
  1873. 194 585.1 KB
  1874. 02_outline-of-first-half-of-course.mp4 19.4 MB
  1875. 195 619.9 KB
  1876. 03_establishing-a-baseline-level-of-performance.mp4 19.4 MB
  1877. 196 629.7 KB
  1878. 01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.mp4 19.4 MB
  1879. 197 633.0 KB
  1880. 01_boosting-a-decision-stump_lending-club-data.gl.zip 19.4 MB
  1881. 198 634.0 KB
  1882. 01_identifying-safe-loans-with-decision-trees_lending-club-data.gl.zip 19.4 MB
  1883. 199 634.0 KB
  1884. 01_decision-trees-in-practice_lending-club-data.gl.zip 19.4 MB
  1885. 200 634.0 KB
  1886. 01_implementing-binary-decision-trees_lending-club-data.gl.zip 19.4 MB
  1887. 201 634.0 KB
  1888. 01_exploring-ensemble-methods_lending-club-data.gl.zip 19.4 MB
  1889. 202 634.0 KB
  1890. 01_identifying-safe-loans-with-decision-trees_lending-club-data.sframe.zip 19.4 MB
  1891. 203 636.2 KB
  1892. 01_boosting-a-decision-stump_lending-club-data.sframe.zip 19.4 MB
  1893. 204 636.2 KB
  1894. 01_decision-trees-in-practice_lending-club-data.sframe.zip 19.4 MB
  1895. 205 636.2 KB
  1896. 01_exploring-ensemble-methods_lending-club-data.sframe.zip 19.4 MB
  1897. 206 636.2 KB
  1898. 01_implementing-binary-decision-trees_lending-club-data.sframe.zip 19.4 MB
  1899. 207 636.2 KB
  1900. 04_gradient-descent-for-multiple-linear-regression.mp4 19.4 MB
  1901. 208 657.6 KB
  1902. 01_application-of-deep-learning-to-computer-vision.mp4 19.3 MB
  1903. 209 707.4 KB
  1904. 01_k-nearest-neighbors-regression.mp4 19.3 MB
  1905. 210 717.2 KB
  1906. 04_random-stochastic-environment-optional.mp4 19.3 MB
  1907. 211 746.4 KB
  1908. 01_how-we-got-into-ml.mp4 19.1 MB
  1909. 212 904.6 KB
  1910. 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.mp4 19.1 MB
  1911. 213 945.2 KB
  1912. 05_linear-classifier-model.mp4 19.1 MB
  1913. 214 947.2 KB
  1914. 04_transfer-learning-using-data-from-a-different-task.mp4 19.0 MB
  1915. 215 1007.5 KB
  1916. 05_feature-by-feature-update.mp4 19.0 MB
  1917. 216 1.0 MB
  1918. 02_variance-and-the-bias-variance-tradeoff.mp4 19.0 MB
  1919. 217 1.0 MB
  1920. 01_error-metrics-for-skewed-datasets.mp4 19.0 MB
  1921. 218 1.0 MB
  1922. 03_decision-boundary.mp4 18.9 MB
  1923. 219 1.1 MB
  1924. 01_clustering-and-similarity-ml-block-diagram.mp4 18.9 MB
  1925. 220 1.1 MB
  1926. 01_mean-normalization.mp4 18.9 MB
  1927. 221 1.1 MB
  1928. 06_regression-trees-optional.mp4 18.9 MB
  1929. 222 1.1 MB
  1930. 01_multiple-features.mp4 18.9 MB
  1931. 223 1.1 MB
  1932. 03_estimating-cluster-parameters-from-known-cluster-assignments.mp4 18.8 MB
  1933. 224 1.2 MB
  1934. 03_exploring-word-counts.mp4 18.7 MB
  1935. 225 1.3 MB
  1936. 04_unsupervised-learning-part-1.mp4 18.7 MB
  1937. 226 1.3 MB
  1938. 05_greedy-algorithms.mp4 18.7 MB
  1939. 227 1.3 MB
  1940. 03_how-to-handle-the-intercept.mp4 18.6 MB
  1941. 228 1.4 MB
  1942. 01_from-weighted-k-nn-to-kernel-regression.mp4 18.6 MB
  1943. 229 1.4 MB
  1944. 05_linear-classifiers.mp4 18.5 MB
  1945. 230 1.5 MB
  1946. 04_mapreduce-for-k-means.mp4 18.5 MB
  1947. 231 1.5 MB
  1948. 01_identifying-safe-loans-with-decision-trees_lending-club-data.csv.zip 18.5 MB
  1949. 232 1.5 MB
  1950. 01_exploring-ensemble-methods_lending-club-data.csv.zip 18.5 MB
  1951. 233 1.5 MB
  1952. 01_implementing-binary-decision-trees_lending-club-data.csv.zip 18.5 MB
  1953. 234 1.5 MB
  1954. 01_decision-trees-in-practice_lending-club-data.csv.zip 18.5 MB
  1955. 235 1.5 MB
  1956. 01_boosting-a-decision-stump_lending-club-data.csv.zip 18.5 MB
  1957. 236 1.5 MB
  1958. 03_what-is-an-intelligent-restaurant-review-system.mp4 18.5 MB
  1959. 237 1.5 MB
  1960. 02_gibbs-sampling-from-10-000-feet.mp4 18.4 MB
  1961. 238 1.6 MB
  1962. 03_putting-it-together.mp4 18.4 MB
  1963. 239 1.6 MB
  1964. 02_loading-exploring-house-sale-data.mp4 18.4 MB
  1965. 240 1.6 MB
  1966. 03_visualizing-the-lasso-cost-and-solution.mp4 18.4 MB
  1967. 241 1.6 MB
  1968. 06_running-gradient-descent.mp4 18.4 MB
  1969. 242 1.6 MB
  1970. 04_approach-2-gradient-descent.mp4 18.4 MB
  1971. 243 1.6 MB
  1972. 04_computing-exploring-tf-idfs.mp4 18.3 MB
  1973. 244 1.7 MB
  1974. 04_data-likelihood.mp4 18.3 MB
  1975. 245 1.7 MB
  1976. 05_goal-of-lda-inference.mp4 18.2 MB
  1977. 246 1.8 MB
  1978. 01_balancing-fit-and-magnitude-of-coefficients.mp4 18.1 MB
  1979. 247 1.9 MB
  1980. 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.mp4 18.0 MB
  1981. 248 5.5 KB
  1982. 03_recommending-from-a-large-catalogue.mp4 18.0 MB
  1983. 249 15.9 KB
  1984. 04_training-evaluating-a-classifier-using-deep-features.mp4 17.9 MB
  1985. 250 138.9 KB
  1986. 05_very-optional-deriving-gradient-of-log-likelihood.mp4 17.8 MB
  1987. 251 153.9 KB
  1988. 05_initializing-k-means.mp4 17.8 MB
  1989. 252 163.4 KB
  1990. 01_visualizing-the-ridge-cost.mp4 17.8 MB
  1991. 253 176.6 KB
  1992. 03_pca-in-code-optional.mp4 17.8 MB
  1993. 254 209.9 KB
  1994. 02_k-means-as-coordinate-descent.mp4 17.8 MB
  1995. 255 247.9 KB
  1996. 03_creating-variables-in-python.mp4 17.7 MB
  1997. 256 321.1 KB
  1998. 01_slides-presented-in-this-module_mixmodel-EM-annotated.pdf 17.7 MB
  1999. 257 325.3 KB
  2000. 02_loading-exploring-wikipedia-data.mp4 17.5 MB
  2001. 258 468.8 KB
  2002. 03_ensemble-classifiers.mp4 17.5 MB
  2003. 259 491.0 KB
  2004. 02_error-analysis.mp4 17.5 MB
  2005. 260 498.7 KB
  2006. 05_when-to-use-decision-trees.mp4 17.5 MB
  2007. 261 540.8 KB
  2008. 02_the-cost-of-using-a-given-line.mp4 17.4 MB
  2009. 262 567.2 KB
  2010. 04_k-nn-algorithm.mp4 17.4 MB
  2011. 263 630.0 KB
  2012. 05_visualizing-the-cost-function.mp4 17.3 MB
  2013. 264 692.2 KB
  2014. 01_threshold-splits-for-continuous-inputs.mp4 17.3 MB
  2015. 265 736.4 KB
  2016. 02_vectorization-part-1.mp4 17.3 MB
  2017. 266 748.4 KB
  2018. 03_vectorization-part-2.mp4 17.3 MB
  2019. 267 760.6 KB
  2020. 01_the-k-means-algorithm.mp4 17.2 MB
  2021. 268 771.3 KB
  2022. 04_discovering-hidden-structure-by-matrix-factorization.mp4 17.2 MB
  2023. 269 800.5 KB
  2024. 03_scaling-mixtures-of-gaussians-for-document-clustering.mp4 17.2 MB
  2025. 270 813.4 KB
  2026. 06_visualization-examples.mp4 17.2 MB
  2027. 271 839.6 KB
  2028. 03_cost-function-with-regularization.mp4 17.1 MB
  2029. 272 920.0 KB
  2030. 03_using-random-lines-to-partition-points.mp4 17.1 MB
  2031. 273 967.7 KB
  2032. 02_more-complex-neural-networks.mp4 17.0 MB
  2033. 274 990.6 KB
  2034. 01_computing-the-gradient-of-rss.mp4 17.0 MB
  2035. 275 996.3 KB
  2036. 01_the-need-for-bayesian-inference.mp4 17.0 MB
  2037. 276 1012.1 KB
  2038. 02_k-fold-cross-validation.mp4 17.0 MB
  2039. 277 1013.0 KB
  2040. 04_effect-of-coefficient-values-on-predicted-probabilities.mp4 17.0 MB
  2041. 278 1.0 MB
  2042. 04_learning-rate.mp4 16.9 MB
  2043. 279 1.1 MB
  2044. 04_overfitting-demo.mp4 16.9 MB
  2045. 280 1.1 MB
  2046. 03_false-positives-false-negatives-and-confusion-matrices.mp4 16.9 MB
  2047. 281 1.1 MB
  2048. 02_gradient-descent-multidimensional-hill-descent.mp4 16.9 MB
  2049. 282 1.1 MB
  2050. 06_choosing-the-number-of-clusters.mp4 16.9 MB
  2051. 283 1.1 MB
  2052. 02_finding-maxima-or-minima-analytically.mp4 16.8 MB
  2053. 284 1.2 MB
  2054. 03_overfitting-in-decision-trees.mp4 16.8 MB
  2055. 285 1.2 MB
  2056. 01_inference-in-code.mp4 16.8 MB
  2057. 286 1.2 MB
  2058. 03_cost-function-formula.mp4 16.7 MB
  2059. 287 1.3 MB
  2060. 02_optional-pruning-decision-trees-to-avoid-overfitting.mp4 16.7 MB
  2061. 288 1.3 MB
  2062. 03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4 16.7 MB
  2063. 289 1.3 MB
  2064. 01_em-iterates-in-equations-and-pictures.mp4 16.7 MB
  2065. 290 1.3 MB
  2066. 03_what-is-a-visual-product-recommender.mp4 16.7 MB
  2067. 291 1.3 MB
  2068. 03_using-the-fitted-line.mp4 16.6 MB
  2069. 292 1.4 MB
  2070. 03_finding-related-items.mp4 16.6 MB
  2071. 293 1.4 MB
  2072. 03_an-alternative-document-clustering-model.mp4 16.6 MB
  2073. 294 1.4 MB
  2074. 01_what-we-covered-and-what-we-didn-t-cover.mp4 16.5 MB
  2075. 295 1.5 MB
  2076. 01_a-performance-metric-for-recommender-systems.mp4 16.5 MB
  2077. 296 1.5 MB
  2078. 03_an-unsupervised-task.mp4 16.5 MB
  2079. 297 1.5 MB
  2080. 04_creating-evaluating-a-personalized-song-recommender.mp4 16.4 MB
  2081. 298 1.6 MB
  2082. 05_gradient-descent-for-linear-regression.mp4 16.4 MB
  2083. 299 1.6 MB
  2084. 05_full-cycle-of-a-machine-learning-project.mp4 16.4 MB
  2085. 300 1.6 MB
  2086. 05_machine-learning-is-changing-the-world.mp4 16.3 MB
  2087. 301 1.7 MB
  2088. 04_choosing-the-learning-rate.mp4 16.3 MB
  2089. 302 1.7 MB
  2090. 03_precision-recall-curves.mp4 16.3 MB
  2091. 303 1.7 MB
  2092. 03_nn-search-with-kd-trees.mp4 16.3 MB
  2093. 304 1.7 MB
  2094. 02_linear-regression-model-part-2.mp4 16.2 MB
  2095. 305 1.8 MB
  2096. 01_ensemble-methods-impact-of-boosting-quick-recap.mp4 16.2 MB
  2097. 306 1.8 MB
  2098. 06_outlining-the-second-half-of-the-course.mp4 16.2 MB
  2099. 307 1.8 MB
  2100. 03_matrix-multiplication-rules.mp4 16.1 MB
  2101. 308 1.9 MB
  2102. 02_feature-split-selection-with-missing-data.mp4 16.1 MB
  2103. 309 1.9 MB
  2104. 06_decision-boundaries.mp4 16.0 MB
  2105. 310 2.0 MB
  2106. 01_measuring-purity.mp4 16.0 MB
  2107. 311 34.3 KB
  2108. 02_matrix-multiplication.mp4 15.9 MB
  2109. 312 110.0 KB
  2110. 05_continuous-valued-features.mp4 15.9 MB
  2111. 313 116.2 KB
  2112. 04_strategy-2-purification-by-imputing-missing-data.mp4 15.9 MB
  2113. 314 119.1 KB
  2114. 02_whats-a-good-accuracy.mp4 15.8 MB
  2115. 315 189.9 KB
  2116. 09_exploring-the-most-positive-negative-aspects-of-a-product.mp4 15.8 MB
  2117. 316 221.7 KB
  2118. 02_addressing-overfitting.mp4 15.7 MB
  2119. 317 271.5 KB
  2120. 04_finding-the-max-via-hill-climbing.mp4 15.7 MB
  2121. 318 315.4 KB
  2122. 03_precision-recall-curve.mp4 15.7 MB
  2123. 319 350.8 KB
  2124. 01_advanced-optimization.mp4 15.6 MB
  2125. 320 437.0 KB
  2126. 01_precision-fraction-of-positive-predictions-that-are-actually-positive.mp4 15.6 MB
  2127. 321 445.3 KB
  2128. 03_the-lasso-objective-and-its-coefficient-path.mp4 15.5 MB
  2129. 322 464.0 KB
  2130. 02_review-of-basics-of-probabilities.mp4 15.5 MB
  2131. 323 470.9 KB
  2132. 04_when-to-stop-recursing.mp4 15.5 MB
  2133. 324 514.3 KB
  2134. 01_regression-ml-block-diagram.mp4 15.5 MB
  2135. 325 541.9 KB
  2136. 01_review-of-gradient-ascent.mp4 15.5 MB
  2137. 326 555.2 KB
  2138. 02_rewriting-the-single-observation-model-in-vector-notation.mp4 15.4 MB
  2139. 327 569.4 KB
  2140. 01_evaluating-overfitting-via-training-test-split.mp4 15.4 MB
  2141. 328 571.4 KB
  2142. 05_computing-distances-between-wikipedia-articles.mp4 15.4 MB
  2143. 329 632.3 KB
  2144. 04_the-dendrogram.mp4 15.3 MB
  2145. 330 668.6 KB
  2146. 01_collaborative-filtering-people-who-bought-this-also-bought.mp4 15.3 MB
  2147. 331 760.3 KB
  2148. 04_using-the-output-of-collapsed-gibbs-sampling.mp4 15.2 MB
  2149. 332 787.8 KB
  2150. 04_interpreting-the-fitted-line.mp4 15.2 MB
  2151. 333 847.6 KB
  2152. 03_selecting-best-feature-to-split-on.mp4 15.1 MB
  2153. 334 879.5 KB
  2154. 02_coordinate-descent.mp4 15.1 MB
  2155. 335 909.3 KB
  2156. 03_deploying-an-ml-service.mp4 15.1 MB
  2157. 336 912.3 KB
  2158. 04_improved-implementation-of-softmax.mp4 15.1 MB
  2159. 337 969.9 KB
  2160. 03_neural-network-with-softmax-output.mp4 15.0 MB
  2161. 338 995.6 KB
  2162. 03_training-evaluating-a-classifier-using-raw-image-pixels.mp4 15.0 MB
  2163. 339 1.0 MB
  2164. 05_outlining-the-first-half-of-the-course.mp4 14.8 MB
  2165. 340 1.2 MB
  2166. 01_penalizing-large-coefficients-to-mitigate-overfitting.mp4 14.8 MB
  2167. 341 1.2 MB
  2168. 01_iterative-loop-of-ml-development.mp4 14.8 MB
  2169. 342 1.2 MB
  2170. 02_loading-and-exploring-song-data.mp4 14.8 MB
  2171. 343 1.2 MB
  2172. 01_computing-the-gradient-of-the-ridge-objective.mp4 14.8 MB
  2173. 344 1.2 MB
  2174. 01_the-matrix-completion-task.mp4 14.8 MB
  2175. 345 1.2 MB
  2176. 01_decision-tree-model.mp4 14.8 MB
  2177. 346 1.2 MB
  2178. 02_visualizing-the-ridge-solution.mp4 14.7 MB
  2179. 347 1.3 MB
  2180. 06_choosing-stepsize-and-convergence-criteria.mp4 14.7 MB
  2181. 348 1.3 MB
  2182. 03_error-vs-amount-of-data.mp4 14.7 MB
  2183. 349 1.3 MB
  2184. 02_state-action-value-function-example.mp4 14.6 MB
  2185. 350 1.4 MB
  2186. 04_example-recognizing-images.mp4 14.6 MB
  2187. 351 1.4 MB
  2188. 02_overfitting-in-boosting.mp4 14.6 MB
  2189. 352 1.4 MB
  2190. 01_encoding-categorical-inputs.mp4 14.6 MB
  2191. 353 1.4 MB
  2192. 06_calculating-tf-idf-vectors.mp4 14.5 MB
  2193. 354 1.5 MB
  2194. 01_principle-of-occams-razor-learning-simpler-decision-trees.mp4 14.5 MB
  2195. 355 1.5 MB
  2196. 03_mapreduce-execution-overview-and-combiners.mp4 14.4 MB
  2197. 356 1.6 MB
  2198. 02_optional-responsibilities-as-bayes-rule.mp4 14.4 MB
  2199. 357 1.6 MB
  2200. 01_modifying-decision-trees-to-handle-missing-data.mp4 14.4 MB
  2201. 358 1.6 MB
  2202. 01_choosing-step-size.mp4 14.4 MB
  2203. 359 1.6 MB
  2204. 03_supervised-learning-part-2.mp4 14.4 MB
  2205. 360 1.6 MB
  2206. 02_using-stochastic-gradient-for-online-learning.mp4 14.4 MB
  2207. 361 1.6 MB
  2208. 02_feature-scaling-part-2.mp4 14.4 MB
  2209. 362 1.6 MB
  2210. 02_interpreting-the-mixture-of-gaussian-terms.mp4 14.4 MB
  2211. 363 1.6 MB
  2212. 02_who-is-this-specialization-for.mp4 14.4 MB
  2213. 364 1.6 MB
  2214. 02_sampling-with-replacement.mp4 14.3 MB
  2215. 365 1.7 MB
  2216. 04_overfitting-in-classification.mp4 14.2 MB
  2217. 366 1.8 MB
  2218. 02_distance-metrics.mp4 14.2 MB
  2219. 367 1.8 MB
  2220. 02_recommendations-from-known-user-item-features.mp4 14.2 MB
  2221. 368 1.8 MB
  2222. 04_using-one-hot-encoding-of-categorical-features.mp4 14.2 MB
  2223. 369 1.8 MB
  2224. 01_motivating-the-use-of-multiple-inputs.mp4 14.2 MB
  2225. 370 1.8 MB
  2226. 01_overfitting-in-classifiers-leads-to-overconfident-predictions.mp4 14.1 MB
  2227. 371 1.9 MB
  2228. 01_document-representation.mp4 14.1 MB
  2229. 372 1.9 MB
  2230. 02_approach-1-closed-form-solution.mp4 14.1 MB
  2231. 373 1.9 MB
  2232. 01_slides-presented-in-this-module_clustering-intro-annotated.pdf 14.1 MB
  2233. 374 1.9 MB
  2234. 01_gradients-derivatives-in-multiple-dimensions.mp4 14.0 MB
  2235. 375 2.0 MB
  2236. 02_learning-boosted-decision-stumps-with-adaboost.mp4 14.0 MB
  2237. 376 15.1 KB
  2238. 01_summary-and-thank-you.mp4 13.9 MB
  2239. 377 60.5 KB
  2240. 02_supervised-learning-part-1.mp4 13.9 MB
  2241. 378 135.0 KB
  2242. 02_thresholding-ridge-coefficients.mp4 13.9 MB
  2243. 379 139.2 KB
  2244. 03_what-do-we-mean-by-loss.mp4 13.9 MB
  2245. 380 149.1 KB
  2246. 03_weighted-k-nearest-neighbors.mp4 13.8 MB
  2247. 381 192.7 KB
  2248. 03_what-is-good-performance-for-a-classifier.mp4 13.8 MB
  2249. 382 194.4 KB
  2250. 01_implementing-em-for-gaussian-mixtures_images.zip 13.8 MB
  2251. 383 218.2 KB
  2252. 02_starting-a-jupyter-notebook.mp4 13.8 MB
  2253. 384 231.9 KB
  2254. 03_example-of-computing-derivative-for-logistic-regression.mp4 13.8 MB
  2255. 385 253.6 KB
  2256. 02_predicting-loan-defaults-with-decision-trees.mp4 13.7 MB
  2257. 386 269.5 KB
  2258. 04_interpreting-derivative-for-logistic-regression.mp4 13.7 MB
  2259. 387 342.2 KB
  2260. 08_exploring-other-features-of-the-data.mp4 13.7 MB
  2261. 388 347.4 KB
  2262. 03_all-subsets.mp4 13.7 MB
  2263. 389 354.3 KB
  2264. 01_feature-scaling-part-1.mp4 13.6 MB
  2265. 390 370.9 KB
  2266. 02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4 13.6 MB
  2267. 391 395.9 KB
  2268. 01_slides-presented-in-this-module_retrieval-intro-annotated.pdf 13.6 MB
  2269. 392 449.2 KB
  2270. 03_visualizing-effect-of-l2-regularization-in-logistic-regression.mp4 13.6 MB
  2271. 393 452.4 KB
  2272. 04_complexity-of-nn-search-with-kd-trees.mp4 13.5 MB
  2273. 394 497.0 KB
  2274. 05_prioritizing-important-words-with-tf-idf.mp4 13.5 MB
  2275. 395 503.4 KB
  2276. 04_other-regression-examples.mp4 13.5 MB
  2277. 396 533.6 KB
  2278. 03_discussing-the-closed-form-solution.mp4 13.5 MB
  2279. 397 559.7 KB
  2280. 04_building-a-recommender-system-via-classification.mp4 13.4 MB
  2281. 398 597.9 KB
  2282. 04_matrix-multiplication-code.mp4 13.4 MB
  2283. 399 634.6 KB
  2284. 02_approach-1-closed-form-solution.mp4 13.3 MB
  2285. 400 703.9 KB
  2286. 04_querying-the-nearest-neighbors-model-to-retrieve-images.mp4 13.3 MB
  2287. 401 721.5 KB
  2288. 02_deep-learning-performance.mp4 13.2 MB
  2289. 402 780.9 KB
  2290. 02_the-boosting-question.mp4 13.2 MB
  2291. 403 782.8 KB
  2292. 02_mixed-membership-models-for-documents.mp4 13.2 MB
  2293. 404 801.3 KB
  2294. 03_gradient-descent-intuition.mp4 13.2 MB
  2295. 405 814.2 KB
  2296. 02_trading-off-precision-and-recall.mp4 13.2 MB
  2297. 406 819.6 KB
  2298. 03_visualizing-decision-boundaries.mp4 13.2 MB
  2299. 407 820.3 KB
  2300. 08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4 13.2 MB
  2301. 408 829.4 KB
  2302. 03_creating-evaluating-a-popularity-based-song-recommender.mp4 13.2 MB
  2303. 409 866.7 KB
  2304. 02_weighted-error.mp4 13.2 MB
  2305. 410 869.9 KB
  2306. 02_divisive-clustering.mp4 13.1 MB
  2307. 411 944.2 KB
  2308. 02_simple-and-multiple-regression.mp4 13.0 MB
  2309. 412 974.4 KB
  2310. 01_training-and-evaluating-a-classifier.mp4 13.0 MB
  2311. 413 1.0 MB
  2312. 07_examples-of-document-retrieval-in-action.mp4 12.9 MB
  2313. 414 1.1 MB
  2314. 05_tensorflow-implementation-of-content-based-filtering.mp4 12.9 MB
  2315. 415 1.1 MB
  2316. 03_test-error-what-we-can-actually-compute.mp4 12.9 MB
  2317. 416 1.1 MB
  2318. 03_why-do-we-need-activation-functions.mp4 12.9 MB
  2319. 417 1.1 MB
  2320. 03_strategy-1-purification-by-skipping-missing-data.mp4 12.9 MB
  2321. 418 1.1 MB
  2322. 02_learning-a-decision-stump.mp4 12.9 MB
  2323. 419 1.1 MB
  2324. 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.mp4 12.9 MB
  2325. 420 1.1 MB
  2326. 02_lsh-as-an-alternative-to-kd-trees.mp4 12.9 MB
  2327. 421 1.1 MB
  2328. 05_the-future-of-intelligent-applications.mp4 12.8 MB
  2329. 422 1.2 MB
  2330. 01_gradient-descent-implementation.mp4 12.8 MB
  2331. 423 1.2 MB
  2332. 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.sframe.zip 12.7 MB
  2333. 424 1.3 MB
  2334. 01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip 12.7 MB
  2335. 425 1.3 MB
  2336. 01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.sframe.zip 12.7 MB
  2337. 426 1.3 MB
  2338. 01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.gl.zip 12.7 MB
  2339. 427 1.3 MB
  2340. 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.gl.zip 12.7 MB
  2341. 428 1.3 MB
  2342. 01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip 12.7 MB
  2343. 429 1.3 MB
  2344. 03_random-forest-algorithm.mp4 12.7 MB
  2345. 430 1.3 MB
  2346. 04_linear-regression-a-model-based-approach.mp4 12.7 MB
  2347. 431 1.3 MB
  2348. 10_applying-learned-models-to-predict-price-of-an-average-house.mp4 12.7 MB
  2349. 432 1.3 MB
  2350. 02_mars-rover-example.mp4 12.7 MB
  2351. 433 1.3 MB
  2352. 02_challenge-of-missing-data.mp4 12.6 MB
  2353. 434 1.4 MB
  2354. 02_case-study-where-accuracy-is-not-best-metric-for-classification.mp4 12.6 MB
  2355. 435 1.4 MB
  2356. 01_coordinate-descent-for-lasso-normalized-features.mp4 12.6 MB
  2357. 436 1.4 MB
  2358. 03_regression-with-features-of-multiple-inputs.mp4 12.6 MB
  2359. 437 1.4 MB
  2360. 03_inference-making-predictions-forward-propagation.mp4 12.6 MB
  2361. 438 1.4 MB
  2362. 01_using-multiple-decision-trees.mp4 12.5 MB
  2363. 439 1.5 MB
  2364. 05_optional-measuring-convergence.mp4 12.5 MB
  2365. 440 1.5 MB
  2366. 02_l2-regularized-logistic-regression.mp4 12.5 MB
  2367. 441 1.5 MB
  2368. 05_using-apply-for-data-transformation.mp4 12.5 MB
  2369. 442 1.5 MB
  2370. 01_selecting-tuning-parameters-via-cross-validation.mp4 12.4 MB
  2371. 443 1.6 MB
  2372. 01_forward-prop-in-a-single-layer.mp4 12.4 MB
  2373. 444 1.6 MB
  2374. 02_k-means-intuition.mp4 12.4 MB
  2375. 445 1.6 MB
  2376. 01_slides-presented-in-this-module_recommenders-intro-annotated.pdf 12.3 MB
  2377. 446 1.7 MB
  2378. 03_intuition-behind-linear-classifiers.mp4 12.3 MB
  2379. 447 1.7 MB
  2380. 01_recommender-systems-ml-block-diagram.mp4 12.3 MB
  2381. 448 1.7 MB
  2382. 01_how-neural-networks-are-implemented-efficiently.mp4 12.2 MB
  2383. 449 1.8 MB
  2384. 05_lasso-demo.mp4 12.2 MB
  2385. 450 1.8 MB
  2386. 04_exploring-the-most-popular-product.mp4 12.1 MB
  2387. 451 1.9 MB
  2388. 02_the-general-mapreduce-abstraction.mp4 12.1 MB
  2389. 452 1.9 MB
  2390. 03_computing-coefficient-of-each-ensemble-component.mp4 12.1 MB
  2391. 453 1.9 MB
  2392. 03_smart-initialization-via-k-means.mp4 12.1 MB
  2393. 454 1.9 MB
  2394. 03_timeline-of-scalable-machine-learning-stochastic-gradient.mp4 12.1 MB
  2395. 455 1.9 MB
  2396. 04_feature-selection-lasso-and-nearest-neighbor-regression.mp4 12.1 MB
  2397. 456 1.9 MB
  2398. 04_assumed-background.mp4 12.0 MB
  2399. 457 2.0 MB
  2400. 02_the-sigmoid-or-logistic-link-function.mp4 12.0 MB
  2401. 458 2.0 MB
  2402. 04_optional-learning-from-batches-of-data.mp4 12.0 MB
  2403. 459 8.8 KB
  2404. 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.mp4 12.0 MB
  2405. 460 26.3 KB
  2406. 01_alternatives-to-the-sigmoid-activation.mp4 12.0 MB
  2407. 461 44.3 KB
  2408. 02_influence-of-high-leverage-points-exploring-the-data.mp4 11.9 MB
  2409. 462 83.7 KB
  2410. 07_evaluating-a-classifier-the-roc-curve.mp4 11.9 MB
  2411. 463 113.7 KB
  2412. 03_optional-tree-pruning-algorithm.mp4 11.9 MB
  2413. 464 114.1 KB
  2414. 06_visualizing-predictions-of-simple-model-with-matplotlib.mp4 11.9 MB
  2415. 465 133.5 KB
  2416. 01_example-of-adaboost-in-action.mp4 11.9 MB
  2417. 466 133.8 KB
  2418. 02_defining-notation.mp4 11.9 MB
  2419. 467 137.5 KB
  2420. 07_assumed-background.mp4 11.8 MB
  2421. 468 154.7 KB
  2422. 04_reweighing-data-to-focus-on-mistakes.mp4 11.8 MB
  2423. 469 229.1 KB
  2424. 02_simplified-cost-function-for-logistic-regression.mp4 11.7 MB
  2425. 470 263.3 KB
  2426. 05_regression-ml-block-diagram.mp4 11.7 MB
  2427. 471 268.1 KB
  2428. 01_what-is-collapsed-gibbs-sampling.mp4 11.7 MB
  2429. 472 294.2 KB
  2430. 02_k-nearest-neighbors-in-practice.mp4 11.7 MB
  2431. 473 334.2 KB
  2432. 05_defining-which-reviews-have-positive-or-negative-sentiment.mp4 11.6 MB
  2433. 474 385.6 KB
  2434. 01_limitations-of-kd-trees.mp4 11.6 MB
  2435. 475 429.7 KB
  2436. 04_hope-for-unsupervised-learning-and-some-challenge-cases.mp4 11.6 MB
  2437. 476 434.4 KB
  2438. 01_can-we-use-regularization-for-feature-selection.mp4 11.6 MB
  2439. 477 447.1 KB
  2440. 03_polynomial-regression.mp4 11.6 MB
  2441. 478 449.7 KB
  2442. 02_the-feature-selection-task.mp4 11.5 MB
  2443. 479 476.2 KB
  2444. 02_starting-turi-create-loading-an-sframe.mp4 11.5 MB
  2445. 480 480.3 KB
  2446. 01_slides-presented-in-this-module_logistic-regression-model-annotated.pdf 11.5 MB
  2447. 481 517.5 KB
  2448. 01_deciding-what-to-try-next.mp4 11.4 MB
  2449. 482 567.3 KB
  2450. 01_course-overview.mp4 11.4 MB
  2451. 483 583.0 KB
  2452. 03_logistic-regression-model.mp4 11.4 MB
  2453. 484 613.3 KB
  2454. 01_tensorflow-implementation.mp4 11.4 MB
  2455. 485 618.7 KB
  2456. 05_review-of-key-concepts.mp4 11.4 MB
  2457. 486 621.7 KB
  2458. 05_overfitting-for-more-general-multiple-regression-models.mp4 11.3 MB
  2459. 487 688.0 KB
  2460. 03_intuition-behind-maximum-likelihood-estimation.mp4 11.3 MB
  2461. 488 691.4 KB
  2462. 05_classification-with-multiple-outputs-optional.mp4 11.3 MB
  2463. 489 708.6 KB
  2464. 02_limitations-of-parametric-regression.mp4 11.3 MB
  2465. 490 725.8 KB
  2466. 02_clustering-documents-an-unsupervised-learning-task.mp4 11.3 MB
  2467. 491 749.2 KB
  2468. 05_where-we-see-seasonality.mp4 11.3 MB
  2469. 492 754.9 KB
  2470. 05_bringing-it-all-together-featurized-matrix-factorization.mp4 11.3 MB
  2471. 493 758.9 KB
  2472. 05_using-precision-recall-to-compare-recommender-models.mp4 11.2 MB
  2473. 494 771.1 KB
  2474. 04_what-is-the-course-about.mp4 11.2 MB
  2475. 495 829.8 KB
  2476. 01_implementing-em-for-gaussian-mixtures_images.sf.zip 11.2 MB
  2477. 496 841.4 KB
  2478. 02_a-brief-recap.mp4 11.2 MB
  2479. 497 844.7 KB
  2480. 02_careful-with-step-sizes-that-are-too-large.mp4 11.2 MB
  2481. 498 856.7 KB
  2482. 01_recursive-greedy-algorithm.mp4 11.2 MB
  2483. 499 865.9 KB
  2484. 01_deep-learning-ml-block-diagram.mp4 11.1 MB
  2485. 500 877.5 KB
  2486. 04_decision-boundaries.mp4 11.1 MB
  2487. 501 919.2 KB
  2488. 03_comparing-gradient-to-stochastic-gradient.mp4 11.1 MB
  2489. 502 924.2 KB
  2490. 05_challenges-of-deep-learning.mp4 11.1 MB
  2491. 503 949.7 KB
  2492. 03_checking-gradient-descent-for-convergence.mp4 11.0 MB
  2493. 504 1.0 MB
  2494. 06_algorithmic-summary-of-gradient-descent-approach.mp4 11.0 MB
  2495. 505 1.0 MB
  2496. 04_task-of-learning-decision-trees-from-data.mp4 11.0 MB
  2497. 506 1.0 MB
  2498. 01_slides-presented-in-this-module_kmeans-annotated.pdf 10.9 MB
  2499. 507 1.1 MB
  2500. 01_the-boosting-theorem.mp4 10.9 MB
  2501. 508 1.1 MB
  2502. 06_optional-adding-regularization.mp4 10.9 MB
  2503. 509 1.1 MB
  2504. 01_slides-presented-in-this-module_deeplearning-annotated.pdf 10.9 MB
  2505. 510 1.1 MB
  2506. 03_discussing-the-closed-form-solution.mp4 10.8 MB
  2507. 511 1.2 MB
  2508. 03_1-nearest-neighbor-algorithm.mp4 10.8 MB
  2509. 512 1.2 MB
  2510. 02_slides-presented-in-this-module_intro.pdf 10.8 MB
  2511. 513 1.2 MB
  2512. 01_classification-ml-block-diagram.mp4 10.7 MB
  2513. 514 1.3 MB
  2514. 02_the-goal-of-clustering.mp4 10.7 MB
  2515. 515 1.3 MB
  2516. 01_welcome.mp4 10.6 MB
  2517. 516 1.4 MB
  2518. 04_components-of-latent-dirichlet-allocation-model.mp4 10.6 MB
  2519. 517 1.4 MB
  2520. 04_course-overview.mp4 10.6 MB
  2521. 518 1.4 MB
  2522. 02_recall-fraction-of-positive-data-predicted-to-be-positive.mp4 10.6 MB
  2523. 519 1.4 MB
  2524. 03_module-2-recap.mp4 10.5 MB
  2525. 520 1.5 MB
  2526. 04_defining-more-bins.mp4 10.5 MB
  2527. 521 1.5 MB
  2528. 04_interacting-with-columns-of-an-sframe.mp4 10.5 MB
  2529. 522 1.5 MB
  2530. 05_adding-higher-order-effects.mp4 10.4 MB
  2531. 523 1.6 MB
  2532. 02_lunar-lander.mp4 10.4 MB
  2533. 524 1.6 MB
  2534. 03_relationship-to-k-means.mp4 10.4 MB
  2535. 525 1.6 MB
  2536. 01_why-would-stochastic-gradient-ever-work.mp4 10.4 MB
  2537. 526 1.6 MB
  2538. 01_the-online-learning-task.mp4 10.2 MB
  2539. 527 1.8 MB
  2540. 03_rewriting-the-model-for-all-observations-in-matrix-notation.mp4 10.2 MB
  2541. 528 1.8 MB
  2542. 01_defining-our-least-squares-optimization-objective.mp4 10.2 MB
  2543. 529 1.8 MB
  2544. 02_visualizing-overconfident-predictions.mp4 10.2 MB
  2545. 530 1.8 MB
  2546. 05_visualizing-scaling-behavior-of-kd-trees.mp4 10.1 MB
  2547. 531 1.9 MB
  2548. 01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf 10.1 MB
  2549. 532 1.9 MB
  2550. 03_what-is-the-goal-and-how-might-you-naively-address-it.mp4 10.1 MB
  2551. 533 1.9 MB
  2552. 02_approach-1-closed-form-solution.mp4 10.0 MB
  2553. 534 14.8 KB
  2554. 05_finding-best-linear-classifier-with-gradient-ascent.mp4 9.9 MB
  2555. 535 57.2 KB
  2556. 05_visualizing-overfitting-with-high-degree-polynomial-features.mp4 9.9 MB
  2557. 536 75.1 KB
  2558. 06_displaying-other-example-image-retrievals-with-a-python-lambda.mp4 9.9 MB
  2559. 537 86.9 KB
  2560. 06_lsh-in-higher-dimensions.mp4 9.9 MB
  2561. 538 100.3 KB
  2562. 02_linear-classifiers-a-motivating-example.mp4 9.9 MB
  2563. 539 102.9 KB
  2564. 04_using-probabilities-in-classification.mp4 9.9 MB
  2565. 540 104.3 KB
  2566. 01_clustering-documents-task-overview.mp4 9.8 MB
  2567. 541 161.7 KB
  2568. 02_issues-with-high-dimensions-data-scarcity-and-computational-complexity.mp4 9.8 MB
  2569. 542 203.8 KB
  2570. 03_canvas-for-data-visualization.mp4 9.8 MB
  2571. 543 246.9 KB
  2572. 02_choosing-step-size.mp4 9.6 MB
  2573. 544 365.2 KB
  2574. 03_review-of-overfitting-in-regression.mp4 9.6 MB
  2575. 545 412.8 KB
  2576. 06_the-ridge-coefficient-path.mp4 9.6 MB
  2577. 546 445.1 KB
  2578. 03_k-means-a-clustering-algorithm.mp4 9.5 MB
  2579. 547 533.6 KB
  2580. 02_optional-picking-the-best-threshold-to-split-on.mp4 9.5 MB
  2581. 548 540.9 KB
  2582. 02_training-test-curves.mp4 9.5 MB
  2583. 549 550.4 KB
  2584. 02_loading-image-data.mp4 9.4 MB
  2585. 550 594.7 KB
  2586. 05_asymmetric-cost-functions.mp4 9.4 MB
  2587. 551 611.0 KB
  2588. 02_retrieval-as-k-nearest-neighbor-search.mp4 9.4 MB
  2589. 552 634.0 KB
  2590. 06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.mp4 9.4 MB
  2591. 553 636.4 KB
  2592. 03_rule-of-thumb-for-choosing-step-size.mp4 9.4 MB
  2593. 554 651.5 KB
  2594. 03_normalizing-features.mp4 9.4 MB
  2595. 555 661.5 KB
  2596. 04_influence-of-high-leverage-points-removing-high-end-towns.mp4 9.3 MB
  2597. 556 685.0 KB
  2598. 02_assessing-convergence-and-other-lasso-solvers.mp4 9.3 MB
  2599. 557 686.0 KB
  2600. 02_evaluating-a-classifier.mp4 9.3 MB
  2601. 558 758.0 KB
  2602. 01_logistic-regression-with-l2-regularization_amazon_baby_subset.csv.zip 9.2 MB
  2603. 559 859.9 KB
  2604. 01_training-logistic-regression-via-stochastic-gradient-ascent_amazon_baby_subset.csv.zip 9.2 MB
  2605. 560 859.9 KB
  2606. 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.csv.zip 9.2 MB
  2607. 561 859.9 KB
  2608. 06_training-a-sentiment-classifier.mp4 9.1 MB
  2609. 562 949.0 KB
  2610. 05_creating-functions-and-lambdas-in-python.mp4 9.1 MB
  2611. 563 964.4 KB
  2612. 04_univariate-gaussian-distributions.mp4 9.0 MB
  2613. 564 1005.0 KB
  2614. 01_slides-presented-in-this-module_johnson_andrew_1997_3.pdf 8.9 MB
  2615. 565 1.1 MB
  2616. 01_why-gradient-ascent-won-t-scale.mp4 8.9 MB
  2617. 566 1.1 MB
  2618. 02_goal-learning-parameters-of-logistic-regression.mp4 8.8 MB
  2619. 567 1.2 MB
  2620. 03_writing-scaled-euclidean-distance-using-weighted-inner-products.mp4 8.8 MB
  2621. 568 1.2 MB
  2622. 01_what-is-clustering.mp4 8.8 MB
  2623. 569 1.2 MB
  2624. 02_stochastic-gradient-learning-one-data-point-at-a-time.mp4 8.8 MB
  2625. 570 1.2 MB
  2626. 07_retrieving-similar-documents-using-nearest-neighbor-search.mp4 8.8 MB
  2627. 571 1.2 MB
  2628. 02_a-worked-example-for-lda-initial-setup.mp4 8.8 MB
  2629. 572 1.2 MB
  2630. 01_slides-presented-in-this-module_week5_lassoregression-annotated.pdf 8.7 MB
  2631. 573 1.3 MB
  2632. 07_using-features-of-the-inputs.mp4 8.7 MB
  2633. 574 1.3 MB
  2634. 05_class-probabilities.mp4 8.7 MB
  2635. 575 1.3 MB
  2636. 03_agglomerative-clustering.mp4 8.7 MB
  2637. 576 1.3 MB
  2638. 01_precision-recall-extremes.mp4 8.6 MB
  2639. 577 1.4 MB
  2640. 06_regression-with-general-features-of-1-input.mp4 8.6 MB
  2641. 578 1.4 MB
  2642. 01_what-we-didn-t-cover.mp4 8.6 MB
  2643. 579 1.4 MB
  2644. 05_finding-the-min-via-hill-descent.mp4 8.5 MB
  2645. 580 1.5 MB
  2646. 04_learning-a-simple-regression-model-to-predict-house-prices-from-house-size.mp4 8.5 MB
  2647. 581 1.5 MB
  2648. 06_complexity-of-the-greedy-forward-stepwise-algorithm.mp4 8.4 MB
  2649. 582 1.6 MB
  2650. 01_what-makes-the-lasso-objective-different.mp4 8.4 MB
  2651. 583 1.6 MB
  2652. 01_multiclass.mp4 8.4 MB
  2653. 584 1.6 MB
  2654. 01_welcome.mp4 8.3 MB
  2655. 585 1.7 MB
  2656. 05_unsupervised-learning-part-2.mp4 8.2 MB
  2657. 586 1.8 MB
  2658. 04_thank-you.mp4 8.2 MB
  2659. 587 1.8 MB
  2660. 02_effect-of-popular-items.mp4 8.0 MB
  2661. 588 2.0 MB
  2662. 02_learning-algorithm-for-logistic-regression.mp4 8.0 MB
  2663. 589 2.0 MB
  2664. 02_loading-exploring-product-review-data.mp4 8.0 MB
  2665. 590 3.8 KB
  2666. 01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf 7.9 MB
  2667. 591 64.1 KB
  2668. 03_demo-of-deep-learning-model-on-imagenet-data.mp4 7.9 MB
  2669. 592 133.2 KB
  2670. 05_overview-of-learning-logistic-regression-models.mp4 7.9 MB
  2671. 593 137.6 KB
  2672. 07_the-state-of-reinforcement-learning.mp4 7.9 MB
  2673. 594 144.6 KB
  2674. 05_feature-engineering.mp4 7.8 MB
  2675. 595 156.2 KB
  2676. 04_algorithm-refinement-improved-neural-network-architecture.mp4 7.8 MB
  2677. 596 214.7 KB
  2678. 01_adaboost-overview.mp4 7.7 MB
  2679. 597 258.1 KB
  2680. 09_learning-a-model-to-predict-house-prices-from-more-features.mp4 7.7 MB
  2681. 598 273.8 KB
  2682. 01_why-hierarchical-clustering.mp4 7.7 MB
  2683. 599 306.5 KB
  2684. 06_effect-of-coefficient-values-on-decision-boundary.mp4 7.7 MB
  2685. 600 355.3 KB
  2686. 01_the-simple-linear-regression-model.mp4 7.6 MB
  2687. 601 361.3 KB
  2688. 03_predictions-in-matrix-form.mp4 7.4 MB
  2689. 602 566.5 KB
  2690. 02_loading-image-data.mp4 7.4 MB
  2691. 603 595.3 KB
  2692. 03_maximizing-a-1d-function-a-worked-example.mp4 7.3 MB
  2693. 604 683.1 KB
  2694. 04_regression-fundamentals-the-task.mp4 7.3 MB
  2695. 605 689.7 KB
  2696. 01_computing-the-gradient-of-rss.mp4 7.3 MB
  2697. 606 764.7 KB
  2698. 02_a-review-of-overfitting.mp4 7.2 MB
  2699. 607 837.6 KB
  2700. 02_thank-you.mp4 7.1 MB
  2701. 608 906.3 KB
  2702. 02_convergence-paths.mp4 7.1 MB
  2703. 609 928.4 KB
  2704. 02_very-optional-expressing-the-log-likelihood.mp4 7.1 MB
  2705. 610 963.5 KB
  2706. 02_multiclass-classification-with-decision-trees.mp4 7.0 MB
  2707. 611 1022.9 KB
  2708. 01_recap-of-learning-logistic-regression-classifiers.mp4 7.0 MB
  2709. 612 1.0 MB
  2710. 01_slides-presented-in-this-module_LDA-annotated.pdf 7.0 MB
  2711. 613 1.0 MB
  2712. 02_symptoms-of-overfitting-in-polynomial-regression.mp4 6.9 MB
  2713. 614 1.1 MB
  2714. 01_slides-presented-in-this-module_classification-annotated.pdf 6.9 MB
  2715. 615 1.1 MB
  2716. 04_complexity-of-all-subsets.mp4 6.8 MB
  2717. 616 1.2 MB
  2718. 01_slides-presented-in-this-module_week3_assessingperformance-annotated.pdf 6.8 MB
  2719. 617 1.2 MB
  2720. 03_1-nn-algorithm.mp4 6.8 MB
  2721. 618 1.2 MB
  2722. 05_normalizing-weights.mp4 6.7 MB
  2723. 619 1.3 MB
  2724. 04_other-examples-of-deep-learning-in-computer-vision.mp4 6.7 MB
  2725. 620 1.3 MB
  2726. 03_adding-other-features.mp4 6.7 MB
  2727. 621 1.3 MB
  2728. 03_k-nn-for-classification.mp4 6.6 MB
  2729. 622 1.4 MB
  2730. 01_a-brief-recap.mp4 6.4 MB
  2731. 623 1.6 MB
  2732. 03_what-is-the-document-retrieval-task.mp4 6.4 MB
  2733. 624 1.6 MB
  2734. 02_slides-presented-in-this-module_intro.pdf 6.2 MB
  2735. 625 1.8 MB
  2736. 02_slides-presented-in-this-module_intro.pdf 6.2 MB
  2737. 626 1.8 MB
  2738. 06_comparing-the-approaches.mp4 6.2 MB
  2739. 627 1.8 MB
  2740. 03_creating-the-word-count-vector.mp4 6.2 MB
  2741. 628 1.8 MB
  2742. 01_recap-of-handling-missing-data.mp4 6.2 MB
  2743. 629 1.8 MB
  2744. 03_splitting-the-data-into-training-and-test-sets.mp4 6.2 MB
  2745. 630 1.8 MB
  2746. 05_evaluating-error-rmse-of-the-simple-model.mp4 6.2 MB
  2747. 631 1.8 MB
  2748. 05_summary-of-gradient-ascent-for-logistic-regression.mp4 6.2 MB
  2749. 632 1.8 MB
  2750. 04_defining-overfitting.mp4 6.1 MB
  2751. 633 1.9 MB
  2752. 05_training-test-split.mp4 6.1 MB
  2753. 634 1.9 MB
  2754. 03_don-t-trust-last-coefficients.mp4 6.0 MB
  2755. 635 2.0 MB
  2756. 01_complexity-of-brute-force-search.mp4 5.9 MB
  2757. 636 80.1 KB
  2758. 03_coordinate-descent-for-lasso-unnormalized-features.mp4 5.9 MB
  2759. 637 88.0 KB
  2760. 03_intuition-behind-decision-trees.mp4 5.9 MB
  2761. 638 105.0 KB
  2762. 01_shuffle-data-before-running-stochastic-gradient.mp4 5.9 MB
  2763. 639 108.0 KB
  2764. 02_optimal-recommenders.mp4 5.9 MB
  2765. 640 127.6 KB
  2766. 04_making-decisions-policies-in-reinforcement-learning.mp4 5.8 MB
  2767. 641 196.5 KB
  2768. 02_thank-you.mp4 5.8 MB
  2769. 642 221.0 KB
  2770. 01_scaling-to-huge-datasets-through-parallelization-module-recap.mp4 5.8 MB
  2771. 643 231.6 KB
  2772. 01_predicting-class-probabilities.mp4 5.7 MB
  2773. 644 303.6 KB
  2774. 04_approach-2-gradient-descent.mp4 5.4 MB
  2775. 645 660.1 KB
  2776. 03_how-ridge-regression-balances-bias-and-variance.mp4 5.3 MB
  2777. 646 684.0 KB
  2778. 04_a-brief-recap.mp4 5.2 MB
  2779. 647 783.2 KB
  2780. 01_recap-of-logistic-regression-classifier.mp4 5.2 MB
  2781. 648 820.7 KB
  2782. 01_recap-of-precision-recall.mp4 5.1 MB
  2783. 649 914.9 KB
  2784. 01_a-brief-recap.mp4 5.1 MB
  2785. 650 970.2 KB
  2786. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.json.zip 5.0 MB
  2787. 651 982.6 KB
  2788. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.json.zip 5.0 MB
  2789. 652 982.6 KB
  2790. 01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.json.zip 5.0 MB
  2791. 653 982.6 KB
  2792. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.json.zip 5.0 MB
  2793. 654 982.6 KB
  2794. 02_a-brief-recap.mp4 5.0 MB
  2795. 655 1010.6 KB
  2796. 03_very-optional-deriving-probability-y-1-given-x.mp4 5.0 MB
  2797. 656 1.0 MB
  2798. 03_welcome.mp4 4.9 MB
  2799. 657 1.1 MB
  2800. 01_recap-of-overfitting-and-regularization-in-decision-trees.mp4 4.9 MB
  2801. 658 1.1 MB
  2802. 01_making-predictions-with-decision-trees.mp4 4.8 MB
  2803. 659 1.2 MB
  2804. 03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4 4.7 MB
  2805. 660 1.3 MB
  2806. 04_a-brief-recap.mp4 4.7 MB
  2807. 661 1.3 MB
  2808. 01_predicting-house-prices-assignment_house_images.zip 4.7 MB
  2809. 662 1.3 MB
  2810. 01_download-the-jupyter-notebook-used-in-this-lesson-to-follow-along_house_images.zip 4.7 MB
  2811. 663 1.3 MB
  2812. 05_querying-for-the-most-similar-images-for-car-image.mp4 4.6 MB
  2813. 664 1.4 MB
  2814. 03_welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4 4.6 MB
  2815. 665 1.4 MB
  2816. 02_recommender-systems-overview.mp4 4.6 MB
  2817. 666 1.4 MB
  2818. 01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf 4.5 MB
  2819. 667 1.5 MB
  2820. 06_a-brief-recap.mp4 4.5 MB
  2821. 668 1.5 MB
  2822. 03_what-you-ll-be-able-to-do.mp4 4.4 MB
  2823. 669 1.6 MB
  2824. 01_a-brief-recap.mp4 4.4 MB
  2825. 670 1.6 MB
  2826. 01_slides-presented-in-this-module_closing.pdf 4.3 MB
  2827. 671 1.7 MB
  2828. 05_impact-of-classification.mp4 4.0 MB
  2829. 672 2.0 MB
  2830. 01_a-brief-recap.mp4 3.9 MB
  2831. 673 93.8 KB
  2832. 01_recap-of-overfitting-regularization-in-logistic-regression.mp4 3.8 MB
  2833. 674 170.1 KB
  2834. 02_predicting-house-prices-a-case-study-in-regression.mp4 3.8 MB
  2835. 675 175.3 KB
  2836. 02_a-case-study-in-predicting-house-prices.mp4 3.8 MB
  2837. 676 192.7 KB
  2838. 02_a-brief-recap.mp4 3.7 MB
  2839. 677 271.6 KB
  2840. 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki_map_index_to_word.gl.zip 3.7 MB
  2841. 678 310.2 KB
  2842. 01_choosing-features-and-metrics-for-nearest-neighbor-search_people_wiki_map_index_to_word.gl.zip 3.7 MB
  2843. 679 310.2 KB
  2844. 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki_map_index_to_word.gl.zip 3.7 MB
  2845. 680 310.2 KB
  2846. 01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip 3.7 MB
  2847. 681 310.2 KB
  2848. 01_slides-presented-in-this-module_online-learning-annotated.pdf 3.6 MB
  2849. 682 358.5 KB
  2850. 01_recap-of-decision-trees.mp4 3.4 MB
  2851. 683 583.4 KB
  2852. 07_inspecting-the-model-coefficients-learned.mp4 3.4 MB
  2853. 684 626.9 KB
  2854. 03_welcome-to-this-course-and-specialization.mp4 3.3 MB
  2855. 685 700.1 KB
  2856. 01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip 3.2 MB
  2857. 686 775.4 KB
  2858. 01_slides-presented-in-this-module_decision-trees-overfitting-annotated.pdf 3.1 MB
  2859. 687 921.5 KB
  2860. 01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf 3.0 MB
  2861. 688 1014.2 KB
  2862. 01_slides-presented-in-this-module_logistic-regression-learning-annotated.pdf 3.0 MB
  2863. 689 1017.8 KB
  2864. 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4 2.9 MB
  2865. 690 1.1 MB
  2866. 01_slides-presented-in-this-module_decision-trees-annotated.pdf 2.8 MB
  2867. 691 1.2 MB
  2868. 02_you-ve-made-it.mp4 2.8 MB
  2869. 692 1.2 MB
  2870. 05_lets-get-started.mp4 2.8 MB
  2871. 693 1.2 MB
  2872. 01_slides-presented-in-this-module_logistic-regression-overfitting-annotated.pdf 2.6 MB
  2873. 694 1.4 MB
  2874. 01_predicting-house-prices-assignment_home_data.csv 2.5 MB
  2875. 695 1.5 MB
  2876. 01_slides-presented-in-this-module_boosting-annotated.pdf 2.2 MB
  2877. 696 1.8 MB
  2878. 02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity.mp4 2.1 MB
  2879. 697 1.9 MB
  2880. 01_slides-presented-in-this-module_mikolajczyk_pami05.pdf 2.0 MB

Discussion