Practical Machine Learning in R (2021 Update)

✍ Scribed by Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos, Thomas Karanikiotis, Michail Papamichail and Andreas Symeonidis

• Publisher: leanpub.com
• Year: 2018
• Tongue: English
• Leaves: 160
• Category: Library

✦ Synopsis

Do you want to start using R for crunching machine learning models right from the start with examples? Then this book is for you.

R is an open source programming language and a free environment, mainly used for statistical computing and graphics. You can find information about R in the official website. By searching with the keyword R with other topic-specific words in sites like Google, one can find additional information from sites, blog posts, tutorials, documents etc. Even through R comes with its own environment: command line and graphical interfaces, one can use the popular RStudio, which offers additional graphical functionalities.

Machine Learning (ML) is a subset of Artificial Intelligence (AI) in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

Machine Learning is often closelly related, if not used as an alternate term, to fields like Data Mining (the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems), Pattern Recognition, Statistical Inference or Statistical Learning. All these areas often employ the same methods and perhaps the name changes based on the practitioner’s expertise or the application domain.

✦ Table of Contents

Title Page
Table of Contents
Part I - Introduction
Chapter 1 - Introduction to R
Chapter 2 - Introduction to Machine Learning
Part II - Classification
Chapter 3 - Classification with Decision trees
Chapter 4 - Classification with Naive Bayes
Chapter 5 - Classification with k-Nearest Neighbors
Chapter 6 - Classification with Support Vector Machines
Part III - Data Processing
Chapter 7 - Feature Selection
Chapter 8 - Dimensionality Reduction
Part III - Clustering
Chapter 9 - Centroid-based Clustering and Evaluation
Chapter 10 - Connectivity-based Clustering
Chapter 11 Density-based Clustering
Chapter 12 - Distribution-based Clustering
Part V - Extended Topics
Chapter 13 - Association Rules