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

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