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