Book description
Building Machine Learning applications with R
About This Book
 Implement a wide range of algorithms and techniques for tackling complex data
 Improve predictions and recommendations to have better levels of accuracy
 Optimize performance of your machinelearning systems
Who This Book Is For
This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.
What You Will Learn
 Get equipped with a deeper understanding of how to apply machinelearning techniques
 Implement each of the advanced machinelearning techniques
 Solve reallife problems that are encountered in order to make your applications produce improved results
 Gain handson experience in problem solving for your machinelearning systems
 Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance
In Detail
Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on realworld challenges you face will help you appreciate a variety of techniques used in various situations.
The first half of the book provides recipes on fairly complex machinelearning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.
The second half of the book focuses on three different machine learning case studies, all based on realworld data, and offers solutions and solves specific machinelearning issues in each one.
Style and approach
Following a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.
Publisher resources
Table of contents

Practical Machine Learning Cookbook
 Practical Machine Learning Cookbook
 Credits
 About the Author
 About the Reviewer
 www.PacktPub.com
 Customer Feedback
 Preface
 1. Introduction to Machine 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
 4. Model Selection and Regularization
 5. Nonlinearity

6. Supervised Learning
 Introduction
 Decision tree learning  Advance Health Directive for patients with chest pain
 Decision tree learning  incomebased 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
 8. Reinforcement Learning
 9. Structured Prediction
 10. Neural Networks
 11. Deep Learning
 12. Case Study  Exploring World Bank Data
 13. Case Study  Pricing Reinsurance Contracts
 14. Case Study  Forecast of Electricity Consumption
Product information
 Title: Practical Machine Learning Cookbook
 Author(s):
 Release date: April 2017
 Publisher(s): Packt Publishing
 ISBN: 9781785280511
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