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Tripathi - Practical machine learning cookbook: resolving and offering solutions to your machine learning problems with R

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Tripathi Practical machine learning cookbook: resolving and offering solutions to your machine learning problems with R
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Introduction to machine learning -- Classification -- Clustering -- Model selection and regularization -- Nonlinearity -- Supervised learning -- Unsupervised learning -- Reinforecement learning -- Structured prediction -- Neural networks -- Deep learning -- Case study-exploring World Bank data -- Case study-pricing reinsurance contracts -- Case study-forecast of electricity consumption.

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Practical Machine Learning Cookbook

Practical Machine Learning Cookbook

Copyright 2017 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: April 2017

Production reference: 1070417

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-78528-051-1

www.packtpub.com

Credits

Author

Atul Tripathi

Copy Editor

Safis Editing

Reviewer

Ryota Kamoshida

Project Coordinator

Nidhi Joshi

Commissioning Editor

Akram Hussain

Proofreader

Safis Editing

Acquisition Editor

Tushar Gupta

Indexer

Tejal Daruwale Soni

Content Development Editor

Aishwarya Pandere

Graphics

Tania Dutta

Technical Editor

Prasad Ramesh

Production Coordinator

Shantanu Zagade

About the Author

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

About the Reviewer

Ryota Kamoshida is the developer of the Python library MALSS ( MAchine Learning Support System ), (https://github.com/canard0328/malss) and now works as a senior researcher in the field of computer science at Hitachi, Ltd.

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Preface

Data in todays world is the new black gold which is growing exponentially. This growth can be attributed to the growth of existing data, and new data in a structured and unstructured format from multiple sources such as social media, Internet, documents and the Internet of Things. The flow of data must be collected, processed, analyzed, and finally presented in real time to ensure that the consumers of the data are able to take informed decisions in todays fast-changing environment. Machine learning techniques are applied to the data using the context of the problem to be solved to ensure that fast arriving and complex data can be analyzed in a scientific manner using statistical techniques. Using machine learning algorithms that iteratively learn from data, hidden patterns can be discovered. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt and learn to produce reliable decisions from new data sets.

We will start by introducing the various topics of machine learning, that will be covered in the book. Based on real-world challenges, we explore each of the topics under various chapters, such as Classification, Clustering, Model Selection and Regularization, Nonlinearity, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Structured Prediction, Neural Networks, Deep Learning, and finally the case studies. The algorithms have been developed using R as the programming language. This book is friendly for beginners in R, but familiarity with R programming would certainly be helpful for playing around with the code.

You will learn how to make informed decisions about the type of algorithms you need to use and how to implement these algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on machine learning will definitely come to your rescue!

What this book covers

, Introduction to Machine Learning, covers various concepts about machine learning. This chapter makes the reader aware of the various topics we shall be covering in the book.

, Classification , covers the following topics and algorithms: discriminant function analysis, multinomial logistic regression, Tobit regression, and Poisson regression.

, Clustering , covers the following topics and algorithms: hierarchical clustering, binary clustering, and k-means clustering.

, Model Selection and Regularization , covers the following topics and algorithms: shrinkage methods, dimension reduction methods, and principal component analysis.

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