Machine Learning Using R

This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.

This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.

For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.

All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.

Who This Book is For

Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

What you will learn

• ML model building process flow
• Theoretical aspects of Machine Learning
• Industry based Case-Study
• Example based understanding of ML algorithm using R
• Building ML models using Apache Hadoop and Spark

Table of Contents

Chapter 1: Introduction to Machine Learning and R
Chapter 2: Data Preparation and Exploration
Chapter 3: Sampling and Resampling Techniques
Chapter 4: Data Visualization in R
Chapter 5: Feature Engineering
Chapter 6: Machine Learning Theory and Practices
Chapter 7: Machine Learning Model Evaluation
Chapter 8: Model Performance Improvement
Chapter 9: Scalable Machine Learning and Related Technologies