Machine Learning Using R / by Karthik Ramasubramanian, Abhishek Singh.

Format:

Book

Author/Creator:

Ramasubramanian, Karthik, Author.

Singh, Abhishek, Author.

Language:

English

Subjects (All):

Artificial intelligence.

Computer programming.

Programming languages (Electronic computers).

Database management.

R (Computer language program).

Artificial Intelligence.

Programming Techniques.

Programming Languages, Compilers, Interpreters.

Database Management.

Local Subjects:

Artificial Intelligence.

Programming Techniques.

Programming Languages, Compilers, Interpreters.

Database Management.

Physical Description:

1 online resource (XXIII, 566 p. 209 illus., 155 illus. in color.)

Edition:

1st ed. 2017.

Other Title:

Comprehensive guide to machine learning

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2017.

System Details:

text file

Summary:

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.

Contents:

Chapter 1: Introduction to Machine Learning and R

Chapter 2: Data Preparation and Exploration

Chapter 3: Sampling and Resampling Techniques

Chapter 4: Visualization of Data

Chapter 5: Feature Engineering

Chapter 6: Machine Learning Models: Theory and Practice

Chapter 7: Machine Learning Model Evaluation.-Chapter 8: Model Performance Improvement

Chapter 9: Scalable Machine Learning and related technology.-.

OCLC:

1005138970