Advertisement

Machine Learning Using R

  • Karthik Ramasubramanian
  • Abhishek Singh

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Karthik Ramasubramanian, Abhishek Singh
    Pages 1-29
  3. Karthik Ramasubramanian, Abhishek Singh
    Pages 31-65
  4. Karthik Ramasubramanian, Abhishek Singh
    Pages 67-127
  5. Karthik Ramasubramanian, Abhishek Singh
    Pages 129-179
  6. Karthik Ramasubramanian, Abhishek Singh
    Pages 181-217
  7. Karthik Ramasubramanian, Abhishek Singh
    Pages 219-424
  8. Karthik Ramasubramanian, Abhishek Singh
    Pages 425-464
  9. Karthik Ramasubramanian, Abhishek Singh
    Pages 465-517
  10. Karthik Ramasubramanian, Abhishek Singh
    Pages 519-554
  11. Back Matter
    Pages 555-566

About this book

Introduction

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.

Keywords

Machine Learning Data Exploration Sampling Techniques Data Visualization Feature Engineering Machine Learning Models Scalable Machine Learning

Authors and affiliations

  • Karthik Ramasubramanian
    • 1
  • Abhishek Singh
    • 2
  1. 1.New DelhiIndia
  2. 2.New DelhiIndia

Bibliographic information