Practical Machine Learning Cookbook

封面

版权信息

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

Chapter 1. Introduction to Machine Learning

What is machine learning?

An overview of classification

An overview of clustering

An overview of supervised learning

An overview of unsupervised learning

An overview of reinforcement learning

An overview of structured prediction

An overview of neural networks

An overview of deep learning

Chapter 2. Classification

Introduction

Discriminant function analysis - geological measurements on brines from wells

Multinomial logistic regression - understanding program choices made by students

Tobit regression - measuring the students' academic aptitude

Poisson regression - understanding species present in Galapagos Islands

Chapter 3. Clustering

Introduction

Hierarchical clustering - World Bank sample dataset

Hierarchical clustering - Amazon rainforest burned between 1999-2010

Hierarchical clustering - gene clustering

Binary clustering - math test

K-means clustering - European countries protein consumption

K-means clustering - foodstuff

Chapter 4. Model Selection and Regularization

Introduction

Shrinkage methods - calories burned per day

Dimension reduction methods - Delta's Aircraft Fleet

Principal component analysis - understanding world cuisine

Chapter 5. Nonlinearity

Generalized additive models - measuring the household income of New Zealand

Smoothing splines - understanding cars and speed

Local regression - understanding drought warnings and impact

Chapter 6. Supervised Learning

Introduction

Decision tree learning - Advance Health Directive for patients with chest pain

Decision tree learning - income-based distribution of real estate values

Decision tree learning - predicting the direction of stock movement

Naive Bayes - predicting the direction of stock movement

Random forest - currency trading strategy

Support vector machine - currency trading strategy

Stochastic gradient descent - adult income

Chapter 7. Unsupervised Learning

Introduction

Self-organizing map - visualizing of heatmaps

Vector quantization - image clustering

Chapter 8. Reinforcement Learning

Introduction

Markov chains - the stocks regime switching model

Markov chains - the multi-channel attribution model

Markov chains - the car rental agency service

Continuous Markov chains - vehicle service at a gas station

Monte Carlo simulations - calibrated Hull and White short-rates

Chapter 9. Structured Prediction

Introduction

Hidden Markov models - EUR and USD

Hidden Markov models - regime detection

Chapter 10. Neural Networks

Introduction

Modelling SP 500

Measuring the unemployment rate

Chapter 11. Deep Learning

Introduction

Recurrent neural networks - predicting periodic signals

Chapter 12. Case Study - Exploring World Bank Data

Introduction

Exploring World Bank data

Chapter 13. Case Study - Pricing Reinsurance Contracts

Introduction

Pricing reinsurance contracts

Chapter 14. Case Study - Forecast of Electricity Consumption