2015
O'Reilly Media / Infinite Skills
Andreas C. Mueller
3:46
English

In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python.
You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification.
Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons.

01. Introduction
0101 What To Expect And About The Author
0102 Setup
0103 The Classifier Interface
0104 The Regressor Interface
0105 The Transformer Interface
0106 The Cluster Interface
0107 The Manifold Interface
0108 scikit-Learn Interface Summary
0109 Cross-Validation With Cross_Val_Score
0110 Parameter Searches With GridSearchCV
0111 How To Access Your Working Files
02. Model Complexity, Overfitting And Underfitting
0201 What Is Model Complexity And Overfitting?
0202 Linear Models In-Depth
0203 Kernel SVMs In-Depth
0204 Random Forests In-Depth
0205 Learning Curves For Analyzing Model Complexity
0206 Validation Curves For Analyzing Model Parameters
0207 Efficient Parameter Search With EstimatorCV Objects
03. Pipelines
0301 Motivation Of Using Pipelines
0302 Defining A Pipeline And Basic Usage
0303 Cross-Validation With Pipelines
0304 Parameter Selection With Pipelines
04. Advanced Metrics And Imbalanced Classes
0401 Be Mindful Of Default Metrics
0402 More Evaluation Methods For Classification
0403 AUC
0404 Defining Custom Metrics
05. Model Selection For Unsupervised Learning
0501 Guidelines For Unsupervised Model Selection
0502 Model Selection For Density Models
0503 Model Selection For Clustering
06. Dealing With Categorical Variables, Dictionaries, And Incomplete Data
0601 Why Real Data Is Messy
0602 One-Hot Encoding For Categorical Data
0603 Working With Dictionaries
0604 Handling Incomplete Data
07. Handling Text Data
0701 Motivation
0702 Bag-Of-Words Representations
0703 Text Classification For Sentiment Analysis - Part 1
0704 Text Classification For Sentiment Analysis - Part 2
0705 The Hashing Trick
0706 Other Representations - Distributed Word Representations
08. Out Of Core Learning
0801 The Trade-Offs Of Out Of Core Learning
0802 The scikit-Learn Interface For Out Of Core Learning
0803 Kernel Approximations For Large-Scale Non-Linear Classification
0804 Subsample And Transform - Supervised Transformations For Out Of Core Learning
0805 Application - Out-Of-Core Text Classification
09. Conclusion
0901 Summary
0902 Where To Go From Here

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