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Choosing the Right ML Approach for Your Business Case with ISO IEC 25053 2022 FreeCours

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Files:
  1. Get Bonus Downloads Here.url 183 bytes
  2. 01 - Why the need for an ML process approach.mp4 3.3 MB
  3. 01 - Why the need for an ML process approach.srt 1.1 KB
  4. 01 - Overview of clauses, terms, and definitions (Clauses 3-5).mp4 11.4 MB
  5. 01 - Overview of clauses, terms, and definitions (Clauses 3-5).srt 5.2 KB
  6. 01 - Task and general (Clause 6-6.2).mp4 4.2 MB
  7. 01 - Task and general (Clause 6-6.2).srt 3.4 KB
  8. 02 - Task details (Clauses 6.2.2-6.2.7).mp4 9.0 MB
  9. 02 - Task details (Clauses 6.2.2-6.2.7).srt 7.0 KB
  10. 03 - ML model (Clause 6.3).mp4 9.3 MB
  11. 03 - ML model (Clause 6.3).srt 6.2 KB
  12. 04 - ML data (Clause 6.4).mp4 6.4 MB
  13. 04 - ML data (Clause 6.4).srt 5.7 KB
  14. 05 - Tools and data preparation (Clauses 6.5-6.5.2).mp4 6.2 MB
  15. 05 - Tools and data preparation (Clauses 6.5-6.5.2).srt 4.8 KB
  16. 06 - Support vector machines ML (Clause 6.5.3.5).mp4 7.7 MB
  17. 06 - Support vector machines ML (Clause 6.5.3.5).srt 6.0 KB
  18. 07 - Bayesian ML (Clause 6.5.3.3).mp4 5.7 MB
  19. 07 - Bayesian ML (Clause 6.5.3.3).srt 5.1 KB
  20. 08 - Decision tree ML (Clause 6.5.3.6).mp4 6.8 MB
  21. 08 - Decision tree ML (Clause 6.5.3.6).srt 4.7 KB
  22. 01 - General and FFNN ML (Clauses 6.5.3.2-6.5.3.2.2).mp4 8.8 MB
  23. 01 - General and FFNN ML (Clauses 6.5.3.2-6.5.3.2.2).srt 6.4 KB
  24. 02 - RNN and LSTM ML (Clauses 6.5.3.2.3-6.5.3.2.3.2).mp4 8.2 MB
  25. 02 - RNN and LSTM ML (Clauses 6.5.3.2.3-6.5.3.2.3.2).srt 6.7 KB
  26. 03 - CNN and CapNet ML(Clauses 6.5.3.2.4, 6.5.3.2.7).mp4 10.1 MB
  27. 03 - CNN and CapNet ML(Clauses 6.5.3.2.4, 6.5.3.2.7).srt 7.3 KB
  28. 04 - DBM, structured perceptron, and GAN ML (Clause 6).mp4 14.4 MB
  29. 04 - DBM, structured perceptron, and GAN ML (Clause 6).srt 10.2 KB
  30. 05 - ML optimization methods (Clauses 6.5.4-6.5.4.8).mp4 10.8 MB
  31. 05 - ML optimization methods (Clauses 6.5.4-6.5.4.8).srt 8.1 KB
  32. 06 - ML evaluation metrics (Clauses 6.5.5-6.5.5.8).mp4 10.0 MB
  33. 06 - ML evaluation metrics (Clauses 6.5.5-6.5.5.8).srt 8.7 KB
  34. 01 - General, supervised, and unsupervised (Clauses 7-7.3).mp4 8.7 MB
  35. 01 - General, supervised, and unsupervised (Clauses 7-7.3).srt 6.6 KB
  36. 02 - Semi- and self-supervised (Clauses 7.4-7.5).mp4 9.5 MB
  37. 02 - Semi- and self-supervised (Clauses 7.4-7.5).srt 7.7 KB
  38. 03 - Reinforcement and transfer (Clauses 7.6-7.7).mp4 7.3 MB
  39. 03 - Reinforcement and transfer (Clauses 7.6-7.7).srt 6.2 KB
  40. 01 - Data acquisition and preparation (Clauses 8-8.3).mp4 10.6 MB
  41. 01 - Data acquisition and preparation (Clauses 8-8.3).srt 6.2 KB
  42. 02 - Data preparation details (Clause 8.3).mp4 7.5 MB
  43. 02 - Data preparation details (Clause 8.3).srt 6.8 KB
  44. 03 - Modeling, verification, and validation (Clauses 8.5-8.6).mp4 11.4 MB
  45. 03 - Modeling, verification, and validation (Clauses 8.5-8.6).srt 8.7 KB
  46. 04 - Model deployment and operation (Clauses 8.6-8.7).mp4 8.1 MB
  47. 04 - Model deployment and operation (Clauses 8.6-8.7).srt 5.6 KB
  48. 05 - Machine learning pipeline example (Clause 8.8).mp4 11.9 MB
  49. 05 - Machine learning pipeline example (Clause 8.8).srt 8.6 KB
  50. 01 - Continue developing your ML business cases.mp4 4.3 MB
  51. 01 - Continue developing your ML business cases.srt 1.5 KB
  52. Bonus Resources.txt 70 bytes
  53. AdditionalResources.pdf 27.1 KB

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