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You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

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Preface

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

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

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

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

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

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

7. Unsupervised Learning

Introduction
Self-organizing map - visualizing of heatmaps
Vector quantization - image clustering

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

9. Structured Prediction

Introduction
Hidden Markov models - EUR and USD
Hidden Markov models - regime detection

10. Neural Networks

Introduction
Modelling SP 500
Measuring the unemployment rate

11. Deep Learning

Introduction
Recurrent neural networks - predicting periodic signals

12. Case Study - Exploring World Bank Data

Introduction
Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Introduction
Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

Chapter 10. Neural Networks

In this chapter, we will cover the following recipes:

Modeling S&P 500
Measuring the unemployment rate

The rest of the chapter is locked

You're reading from Practical Machine Learning Cookbook

Table of Contents (21) Chapters

Chapter 10. Neural Networks

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Table of Contents (21) Chapters

Chapter 10. Neural Networks

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