Advanced Machine Learning with scikit-learn

Table of contents

1. Introduction
   1. What To Expect And About The Author 00:03:47
   2. Setup 00:02:14
   3. The Classifier Interface 00:08:29
   4. The Regressor Interface 00:02:53
   5. The Transformer Interface 00:02:11
   6. The Cluster Interface 00:06:04
   7. The Manifold Interface 00:03:32
   8. scikit-Learn Interface Summary 00:04:01
   9. Cross-Validation With Cross_Val_Score 00:06:19
   10. Parameter Searches With GridSearchCV 00:06:14
2. Model Complexity, Overfitting And Underfitting
   1. What Is Model Complexity And Overfitting? 00:02:58
   2. Linear Models In-Depth 00:11:06
   3. Kernel SVMs In-Depth 00:07:40
   4. Random Forests In-Depth 00:06:02
   5. Learning Curves For Analyzing Model Complexity 00:03:54
   6. Validation Curves For Analyzing Model Parameters 00:02:30
   7. Efficient Parameter Search With EstimatorCV Objects 00:05:13
3. Pipelines
   1. Motivation Of Using Pipelines 00:03:09
   2. Defining A Pipeline And Basic Usage 00:06:30
   3. Cross-Validation With Pipelines 00:02:32
   4. Parameter Selection With Pipelines 00:04:37
4. Advanced Metrics And Imbalanced Classes
   1. Be Mindful Of Default Metrics 00:07:04
   2. More Evaluation Methods For Classification 00:05:17
   3. AUC 00:06:45
   4. Defining Custom Metrics 00:05:39
5. Model Selection For Unsupervised Learning
   1. Guidelines For Unsupervised Model Selection 00:06:52
   2. Model Selection For Density Models 00:05:53
   3. Model Selection For Clustering 00:04:44
6. Dealing With Categorical Variables, Dictionaries, And Incomplete Data
   1. Why Real Data Is Messy 00:06:24
   2. One-Hot Encoding For Categorical Data 00:06:24
   3. Working With Dictionaries 00:02:01
   4. Handling Incomplete Data 00:04:15
7. Handling Text Data
   1. Motivation 00:02:51
   2. Bag-Of-Words Representations 00:06:48
   3. Text Classification For Sentiment Analysis - Part 1 00:07:25
   4. Text Classification For Sentiment Analysis - Part 2 00:04:01
   5. The Hashing Trick 00:03:25
   6. Other Representations - Distributed Word Representations 00:02:38
8. Out Of Core Learning
   1. The Trade-Offs Of Out Of Core Learning 00:04:43
   2. The scikit-Learn Interface For Out Of Core Learning 00:05:13
   3. Kernel Approximations For Large-Scale Non-Linear Classification 00:05:06
   4. Subsample And Transform - Supervised Transformations For Out Of Core Learning 00:05:35
   5. Application - Out-Of-Core Text Classification 00:04:58
9. Conclusion
   1. Summary 00:03:29
   2. Where To Go From Here 00:03:26