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Machine Learning Using R

With Time Series and Industry-Based Use Cases in R

  • Karthik Ramasubramanian
  • Abhishek Singh

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Karthik Ramasubramanian, Abhishek Singh
    Pages 1-33
  3. Karthik Ramasubramanian, Abhishek Singh
    Pages 35-77
  4. Karthik Ramasubramanian, Abhishek Singh
    Pages 79-150
  5. Karthik Ramasubramanian, Abhishek Singh
    Pages 151-209
  6. Karthik Ramasubramanian, Abhishek Singh
    Pages 211-251
  7. Karthik Ramasubramanian, Abhishek Singh
    Pages 253-481
  8. Karthik Ramasubramanian, Abhishek Singh
    Pages 483-531
  9. Karthik Ramasubramanian, Abhishek Singh
    Pages 533-593
  10. Karthik Ramasubramanian, Abhishek Singh
    Pages 595-627
  11. Karthik Ramasubramanian, Abhishek Singh
    Pages 629-665
  12. Karthik Ramasubramanian, Abhishek Singh
    Pages 667-688
  13. Back Matter
    Pages 689-700

About this book

Introduction

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.

As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.

You will:

  • Understand machine learning algorithms using R
  • Master the process of building machine-learning models 
  • Cover the theoretical foundations of machine-learning algorithms
  • See industry focused real-world use cases
  • Tackle time series modeling in R
  • Apply deep learning using Keras and TensorFlow in R

Keywords

Machine Learning Data Exploration Sampling Techniques Data Visualization Feature Engineering Machine Learning Models Scalable Machine Learning R Programming Source Code

Authors and affiliations

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

Bibliographic information