Machine Learning Using R : With Time Series and Industry-Based Use Cases in R / by Karthik Ramasubramanian, Abhishek Singh.

Format:

Book

Author/Creator:

Ramasubramanian, Karthik, Author.

Singh, Abhishek, Author.

Language:

English

Subjects (All):

Artificial intelligence.

Open source software.

Computer programming.

Programming languages (Electronic computers).

R (Computer program language).

Artificial Intelligence.

Open Source.

Programming Languages, Compilers, Interpreters.

Local Subjects:

Artificial Intelligence.

Open Source.

Programming Languages, Compilers, Interpreters.

Physical Description:

1 online resource (712 pages)

Edition:

2nd ed. 2019.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

text file

Summary:

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.

Contents:

Chapter 1: Introduction to Machine Learning

Chapter 2: Data Exploration and Preparation

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: Time Series Modelling

Chapter 10: Scalable Machine Learning and related technology

Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow.

Notes:

Includes index.

ISBN:

9781523150403

1523150408

9781484242155

1484242157

OCLC:

1085513890