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[CourseClub.NET] Coursera - Applied Machine Learning in Python

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  2. File Size：902.21 MB
  3. Creat Time：2024-07-02
  4. Active Degree：23
  5. Last Active：2024-11-23
  6. File Tags：CourseClub  NET  Coursera  Applied  Machine  Learning  in  Python  
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File List:

    [CourseClub.NET] Coursera - Applied Machine Learning in Python

  1. 003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 47.20 MB
  2. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 45.62 MB
  3. 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 42.51 MB
  4. 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 40.89 MB
  5. 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 40.08 MB
  6. 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 38.79 MB
  7. 002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 38.74 MB
  8. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 37.12 MB
  9. 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 35.33 MB
  10. 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 33.68 MB
  11. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 33.01 MB
  12. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 32.49 MB
  13. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 31.79 MB
  14. 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 30.80 MB
  15. 005.Optional Unsupervised Machine Learning/034. Clustering.mp4 27.83 MB
  16. 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 27.08 MB
  17. 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 23.23 MB
  18. 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 23.07 MB
  19. 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 21.89 MB
  20. 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 21.25 MB
  21. 002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20.78 MB
  22. 002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 20.47 MB
  23. 003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 20.24 MB
  24. 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 19.97 MB
  25. 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 17.88 MB
  26. 003.Module 3 Evaluation/024. Regression Evaluation.mp4 17.42 MB
  27. 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16.47 MB
  28. 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 15.78 MB
  29. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 13.17 MB
  30. 003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 12.96 MB
  31. 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 12.09 MB
  32. 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11.49 MB
  33. 005.Optional Unsupervised Machine Learning/032. Introduction.mp4 10.92 MB
  34. 006.Conclusion/035. Conclusion.mp4 10.13 MB
  35. 003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9.45 MB
  36. 003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 30 KB
  37. 002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 29 KB
  38. 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 28 KB
  39. 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 27 KB
  40. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 26 KB
  41. 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 26 KB
  42. 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 22 KB
  43. 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 21 KB
  44. 005.Optional Unsupervised Machine Learning/034. Clustering.srt 20 KB
  45. 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 19 KB
  46. 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 18 KB
  47. 002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 17 KB
  48. 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 17 KB
  49. 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 17 KB
  50. 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 17 KB
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