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You're reading from Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781838550134

Length 416 pages

Edition 1st Edition

Languages

Tools

RStudio

Concepts

Machine Learning

Authors (3):

Brindha Priyadarshini Jeyaraman

Ludvig Renbo Olsen

Monicah Wambugu

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Table of Contents (8) Chapters

About the Book

About the Authors
Description
Learning Objectives
Audience
Approach
Minimum Hardware Requirements
Software Requirements
Conventions
Installation and Setup
Installing R
Installing R Studio
Installing Libraries
Installing the Code Bundle
Additional Resources

1. An Introduction to Machine Learning FREE CHAPTER

Introduction
The Machine Learning Process
Introduction to R
Machine Learning Models
Regression
Summary

2. Data Cleaning and Pre-processing

Introduction
Advanced Operations on Data Frames
Identifying the Input and Output Variables
Identifying the Category of Prediction
Handling Missing Values, Duplicates, and Outliers
Handling Outliers
Summary

3. Feature Engineering

Introduction
Types of Features
Time Series Features
Handling Categorical Variables
Derived Features or Domain-Specific Features
Adding Features to a Data Frame
Handling Redundant Features
Feature Selection
Summary

4. Introduction to neuralnet and Evaluation Methods

Introduction
Classification
Model Selection
Multiclass Classification Overview
Summary

5. Linear and Logistic Regression Models

Introduction
Regression
Linear Regression
Logistic Regression
Regression and Classification with Decision Trees
Model Selection by Multiple Disagreeing Metrics
Summary

6. Unsupervised Learning

Introduction
Overview of Unsupervised Learning (Clustering)
DIANA
Applications of Clustering
k-means Clustering
Summary

1. Appendix

Chapter 1: An Introduction to Machine Learning
Chapter 2: Data Cleaning and Pre-processing
Chapter 3: Feature Engineering
Chapter 4: Introduction to neuralnet and Evaluation Methods
Chapter 5: Linear and Logistic Regression Models
Chapter 6: Unsupervised Learning

Minimum Hardware Requirements

For the optimal student experience, we recommend the following hardware configuration:

Processor: Intel Core i5 or equivalent
Memory: 4GB RAM (8 GB Preferred)
Storage: 16 GB available space

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You're reading from Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Table of Contents (8) Chapters

Minimum Hardware Requirements

Authors (3)

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You're reading from Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Table of Contents (8) Chapters

Minimum Hardware Requirements

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Authors (3)

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