Mastering Machine Learning with R: Advanced prediction, algorithms, and learning methods with R 3.x, 2nd Edition

Master machine learning techniques with R to deliver insights in complex projects

Key Features

• Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST
• Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning
• Implement advanced concepts in machine learning with this example-rich guide

Book Description

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.

You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do.

With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

What you will learn

• Gain deep insights into the application of machine learning tools in the industry
• Manipulate data in R efficiently to prepare it for analysis
• Master the skill of recognizing techniques for effective visualization of data
• Understand why and how to create test and training data sets for analysis
• Master fundamental learning methods such as linear and logistic regression
• Comprehend advanced learning methods such as support vector machines
• Learn how to use R in a cloud service such as Amazon

Table of Contents

1. A Process for Success
2. Linear Regression - The Blocking and Tackling of Machine Learning
3. Logistic Regression and Discriminant Analysis
4. Advanced Feature Selection in Linear Models
5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
6. Classification and Regression Trees
7. Neural Networks and Deep Learning
8. Cluster Analysis
9. Principal Components Analysis
10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis
11. Creating Ensembles and Multiclass Classification
12. Time Series and Causality
13. Text Mining
14. R on the Cloud
15. R Fundamentals
16. Sources

Cory Lesmeister has over a dozen years of quantitative experience and is currently a Senior Quantitative Manager in the banking industry, responsible for building marketing and regulatory models. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. A former U.S. Army active duty and reserve officer, Cory was in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, where he supplied equipment to help the country secure and protect its oil infrastructure. An aviation aficionado, Cory has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license.