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: 

1.ML model building process flow

2.Theoretical aspects of Machine Learning

3.Industry based Case-Study

4.Example based understanding of ML algorithm using R

5.Building ML models using Apache Hadoop and Spark

Karthik Ramasubramanian, works for one of the largest and fastest growing technology unicorn in India, Hike Messenger. He brings the best of Business Analytics and Data Science experience to his role at Hike Messenger. In his 7 years of research and industry experience, he has worked on cross-industry data science problems in retail, e-commerce, and technology, developing and prototyping data driven solutions. In his previous role at Snapdeal, one of the largest e-commerce retailer in India, he was leading core statistical modelling initiatives for customer growth and pricing analytics. Prior to Snapdeal, he was part of central database team, managing the data warehouses for global business applications of Reckitt Benckiser (RB). He has rich experience working with scalable machine learning solutions for industry, including sophisticated graph network and self-learning neural networks. He has a Masters in Theoretical Computer Science from PSG College of Technology, Anna University and certified big data professional. He is passionate about teaching and mentoring future data scientist through different online and public forums. He enjoys writing poems in his leisure time and an avid traveler.