Books & Videos

Table of Contents

Chapter: Introduction

What To Expect And About The Author

03m 47s


02m 13s

The Classifier Interface

08m 29s

The Regressor Interface

02m 53s

The Transformer Interface

02m 11s

The Cluster Interface

06m 4s

The Manifold Interface

03m 32s

scikit-Learn Interface Summary

04m 1s

Cross-Validation With Cross_Val_Score

06m 18s

Parameter Searches With GridSearchCV

06m 13s

How To Access Your Working Files

01m 15s

Chapter: Model Complexity, Overfitting And Underfitting

What Is Model Complexity And Overfitting?

02m 58s

Linear Models In-Depth

11m 5s

Kernel SVMs In-Depth

07m 40s

Random Forests In-Depth

06m 2s

Learning Curves For Analyzing Model Complexity

03m 53s

Validation Curves For Analyzing Model Parameters

02m 30s

Efficient Parameter Search With EstimatorCV Objects

05m 12s

Chapter: Pipelines

Motivation Of Using Pipelines

03m 9s

Defining A Pipeline And Basic Usage

06m 29s

Cross-Validation With Pipelines

02m 31s

Parameter Selection With Pipelines

04m 36s

Chapter: Advanced Metrics And Imbalanced Classes

Be Mindful Of Default Metrics

07m 4s

More Evaluation Methods For Classification

05m 16s


06m 45s

Defining Custom Metrics

05m 38s

Chapter: Model Selection For Unsupervised Learning

Guidelines For Unsupervised Model Selection

06m 52s

Model Selection For Density Models

05m 53s

Model Selection For Clustering

04m 44s

Chapter: Dealing With Categorical Variables, Dictionaries, And Incomplete Data

Why Real Data Is Messy

06m 23s

One-Hot Encoding For Categorical Data

06m 24s

Working With Dictionaries

02m 1s

Handling Incomplete Data

04m 15s

Chapter: Handling Text Data


02m 51s

Bag-Of-Words Representations

06m 48s

Text Classification For Sentiment Analysis - Part 1

07m 25s

Text Classification For Sentiment Analysis - Part 2

04m 0s

The Hashing Trick

03m 25s

Other Representations - Distributed Word Representations

02m 38s

Chapter: Out Of Core Learning

The Trade-Offs Of Out Of Core Learning

04m 43s

The scikit-Learn Interface For Out Of Core Learning

05m 12s

Kernel Approximations For Large-Scale Non-Linear Classification

05m 6s

Subsample And Transform - Supervised Transformations For Out Of Core Learning

05m 35s

Application - Out-Of-Core Text Classification

04m 57s

Chapter: Conclusion


03m 29s

Where To Go From Here

03m 26s