Supervised Machine Learning with Python电子书

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Preface

Who this book is for

What this book covers

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Conventions used

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First Step Towards Supervised Learning

Technical requirements

An example of supervised learning in action

Logistic regression

Setting up the environment

Supervised learning

Hill climbing and loss functions

Loss functions

Measuring the slope of a curve

Measuring the slope of an Nd-curve

Measuring the slope of multiple functions

Hill climbing and descent

Model evaluation and data splitting

Out-of-sample versus in-sample evaluation

Splitting made easy

Summary

Implementing Parametric Models

Technical requirements

Parametric models

Finite-dimensional models

The characteristics of parametric learning algorithms

Parametric model example

Implementing linear regression from scratch

The BaseSimpleEstimator interface

Logistic regression models

The concept

The math

The logistic (sigmoid) transformation

The algorithm

Creating predictions

Implementing logistic regression from scratch

Example of logistic regression

The pros and cons of parametric models

Summary

Working with Non-Parametric Models

Technical requirements

The bias/variance trade-off

Error terms

Error due to bias

Error due to variance

Learning curves

Strategies for handling high bias

Strategies for handling high variance

Introduction to non-parametric models and decision trees

Non-parametric learning

Characteristics of non-parametric learning algorithms

Is a model parametric or not?

An intuitive example – decision tree

Decision trees – an introduction

How do decision trees make decisions?

Decision trees

Splitting a tree by hand

If we split on x1

If we split on x2

Implementing a decision tree from scratch

Classification tree

Regression tree

Various clustering methods

What is clustering?

Distance metrics

KNN – introduction

KNN – considerations

A classic KNN algorithm

Implementing KNNs from scratch

KNN clustering

Non-parametric models – pros/cons

Pros of non-parametric models

Cons of non-parametric models

Which model to use?

Summary

Advanced Topics in Supervised Machine Learning

Technical requirements

Recommended systems and an introduction to collaborative filtering

Item-to-item collaborative filtering

Matrix factorization

Matrix factorization in Python

Limitations of ALS

Content-based filtering

Limitations of content-based systems

Neural networks and deep learning

Tips and tricks for training a neural network

Neural networks

Using transfer learning

Summary

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