Advanced Machine Learning with Python电子书

Advanced Machine Learning with Python

Table of Contents

Advanced Machine Learning with Python

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

Preface

What is advanced machine learning?

What should you expect from this book?

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Unsupervised Machine Learning

Principal component analysis

PCA – a primer

Employing PCA

Introducing k-means clustering

Clustering – a primer

Kick-starting clustering analysis

Tuning your clustering configurations

Self-organizing maps

SOM – a primer

Employing SOM

Further reading

Summary

2. Deep Belief Networks

Neural networks – a primer

The composition of a neural network

Network topologies

Restricted Boltzmann Machine

Introducing the RBM

Topology

Training

Applications of the RBM

Further applications of the RBM

Deep belief networks

Training a DBN

Applying the DBN

Validating the DBN

Further reading

Summary

3. Stacked Denoising Autoencoders

Autoencoders

Introducing the autoencoder

Topology

Training

Denoising autoencoders

Applying a dA

Stacked Denoising Autoencoders

Applying the SdA

Assessing SdA performance

Further reading

Summary

4. Convolutional Neural Networks

Introducing the CNN

Understanding the convnet topology

Understanding convolution layers

Understanding pooling layers

Training a convnet

Putting it all together

Applying a CNN

Further Reading

Summary

5. Semi-Supervised Learning

Introduction

Understanding semi-supervised learning

Semi-supervised algorithms in action

Self-training

Implementing self-training

Finessing your self-training implementation

Improving the selection process

Contrastive Pessimistic Likelihood Estimation

Further reading

Summary

6. Text Feature Engineering

Introduction

Text feature engineering

Cleaning text data

Text cleaning with BeautifulSoup

Managing punctuation and tokenizing

Tagging and categorising words

Tagging with NLTK

Sequential tagging

Backoff tagging

Creating features from text data

Stemming

Bagging and random forests

Testing our prepared data

Further reading

Summary

7. Feature Engineering Part II

Introduction

Creating a feature set

Engineering features for ML applications

Using rescaling techniques to improve the learnability of features

Creating effective derived variables

Reinterpreting non-numeric features

Using feature selection techniques

Performing feature selection

Correlation

LASSO

Recursive Feature Elimination

Genetic models

Feature engineering in practice

Acquiring data via RESTful APIs

Testing the performance of our model

Twitter

Translink Twitter

Consumer comments

The Bing Traffic API

Deriving and selecting variables using feature engineering techniques

The weather API

Further reading

Summary

8. Ensemble Methods

Introducing ensembles

Understanding averaging ensembles

Using bagging algorithms

Using random forests

Applying boosting methods

Using XGBoost

Using stacking ensembles

Applying ensembles in practice

Using models in dynamic applications

Understanding model robustness

Identifying modeling risk factors

Strategies to managing model robustness

Further reading

Summary

9. Additional Python Machine Learning Tools

Alternative development tools

Introduction to Lasagne

Getting to know Lasagne

Introduction to TensorFlow

Getting to know TensorFlow

Using TensorFlow to iteratively improve our models

Knowing when to use these libraries

Further reading

Summary

A. Chapter Code Requirements

Index