Machine Learning Using R With Time Series and Industry-Based Use Cases in R /

• Main Authors: Ramasubramanian, Karthik (Author, http://id.loc.gov/vocabulary/relators/aut), Singh, Abhishek (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berkeley, CA : Apress : Imprint: Apress, 2019.
• Edition: 2nd ed. 2019.
• Subjects: Artificial intelligence.; Open source software.; Computer programming.; Programming languages (Electronic computers).; Artificial Intelligence.; Open Source.; Programming Languages, Compilers, Interpreters.

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 avo...

Table of 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.