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7. Typical scikit-learn Workflow.mp4 190M
8. Optional Debugging Warnings In Jupyter.mp4 176M
39. Tuning Hyperparameters.mp4 176M
45. Putting It All Together.mp4 158M
15. Choosing The Right Model For Your Data.mp4 143M
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 137M
11. Getting Your Data Ready Convert Data To Numbers.mp4 135M
41. Tuning Hyperparameters 3.mp4 122M
19. Choosing The Right Model For Your Data 3 (Classification).mp4 119M
46. Putting It All Together 2.mp4 117M
40. Tuning Hyperparameters 2.mp4 117M
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 105M
25. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 96M
37. Evaluating A Model With Scikit-learn Functions.mp4 95M
36. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 92M
38. Improving A Machine Learning Model.mp4 91M
4. Refresher What Is Machine Learning.mp4 88M
31. Evaluating A Classification Model 6 (Classification Report).mp4 87M
24. Evaluating A Machine Learning Model (Score).mp4 87M
16. Choosing The Right Model For Your Data 2 (Regression).mp4 87M
29. Evaluating A Classification Model 4 (Confusion Matrix).mp4 78M
6. Scikit-learn Cheatsheet.mp4 75M
32. Evaluating A Regression Model 1 (R2 Score).mp4 70M
21. Making Predictions With Our Model.srt 67M
21. Making Predictions With Our Model.mp4 67M
27. Evaluating A Classification Model 2 (ROC Curve).mp4 66M
9. Getting Your Data Ready Splitting Your Data.mp4 64M
30. Evaluating A Classification Model 5 (Confusion Matrix).mp4 64M
44. Saving And Loading A Model 2.mp4 57M
20. Fitting A Model To The Data.mp4 57M
34. Evaluating A Regression Model 3 (MSE).mp4 55M
22. predict() vs predict_proba().mp4 54M
43. Saving And Loading A Model.mp4 53M
28. Evaluating A Classification Model 3 (ROC Curve).mp4 51M
23. Making Predictions With Our Model (Regression).mp4 45M
2. Scikit-learn Introduction.mp4 41M
26. Evaluating A Classification Model 1 (Accuracy).mp4 31M
33. Evaluating A Regression Model 2 (MAE).mp4 29M
42. Quick Tip Correlation Analysis.mp4 17M
10. Quick Tip Clean, Transform, Reduce.mp4 17M
1. Section Overview.mp4 12M
18. Quick Tip How ML Algorithms Work.mp4 11M
7. Typical scikit-learn Workflow.srt 32K
39. Tuning Hyperparameters.srt 31K
45. Putting It All Together.srt 26K
8. Optional Debugging Warnings In Jupyter.srt 26K
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt 23K
11. Getting Your Data Ready Convert Data To Numbers.srt 23K
15. Choosing The Right Model For Your Data.srt 21K
9.1 scikit-learn-data.zip 21K
41. Tuning Hyperparameters 3.srt 19K
36. Evaluating A Model With Cross Validation and Scoring Parameter.srt 18K
25. Evaluating A Machine Learning Model 2 (Cross Validation).srt 17K
19. Choosing The Right Model For Your Data 3 (Classification).srt 17K
40. Tuning Hyperparameters 2.srt 17K
12. Getting Your Data Ready Handling Missing Values With Pandas.srt 17K
37. Evaluating A Model With Scikit-learn Functions.srt 16K
46. Putting It All Together 2.srt 16K
29. Evaluating A Classification Model 4 (Confusion Matrix).srt 15K
38. Improving A Machine Learning Model.srt 15K
31. Evaluating A Classification Model 6 (Classification Report).srt 15K
24. Evaluating A Machine Learning Model (Score).srt 13K
27. Evaluating A Classification Model 2 (ROC Curve).srt 12K
9. Getting Your Data Ready Splitting Your Data.srt 12K
32. Evaluating A Regression Model 1 (R2 Score).srt 12K
16. Choosing The Right Model For Your Data 2 (Regression).srt 12K
22. predict() vs predict_proba().srt 12K
30. Evaluating A Classification Model 5 (Confusion Matrix).srt 11K
2. Scikit-learn Introduction.srt 11K
6. Scikit-learn Cheatsheet.srt 10K
28. Evaluating A Classification Model 3 (ROC Curve).srt 10K
43. Saving And Loading A Model.srt 9.8K
20. Fitting A Model To The Data.srt 9.3K
34. Evaluating A Regression Model 3 (MSE).srt 9.2K
23. Making Predictions With Our Model (Regression).srt 9.1K
44. Saving And Loading A Model 2.srt 9.0K
35. Machine Learning Model Evaluation.html 7.1K
10. Quick Tip Clean, Transform, Reduce.srt 6.4K
4. Refresher What Is Machine Learning.srt 6.3K
26. Evaluating A Classification Model 1 (Accuracy).srt 5.9K
33. Evaluating A Regression Model 2 (MAE).srt 5.7K
1. Section Overview.srt 4.1K
42. Quick Tip Correlation Analysis.srt 3.1K
13. Note Correction in the upcoming video (splitting data).html 2.2K
47. Scikit-Learn Practice.html 2.1K
18. Quick Tip How ML Algorithms Work.srt 1.9K
5. Quick Note Upcoming Videos.html 1.0K
3. Quick Note Upcoming Video.html 390
17. Quick Note Decision Trees.html 221
46.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html 197
2.2 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html 197
6.1 Scikit-Learn Reference Notebook.html 194
7.1 Example Scikit-Learn Workflow Notebook.html 192
46.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html 191
2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html 191
15.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html 133
2.1 Scikit-Learn Documentation.html 108