Machine Learning with R
With the expert help of Brett Lantz, you’ll learn how to uncover key insights and make new predictions using this hands-on, practical guide to machine learning with R. This 10th Anniversary Edition features an overview of R and plenty of new use cases for advanced users. The book is fully updated to R 4.0.0, with newer and better examples and the most up-to-date R libraries, advice on ethical and bias issues, and new chapters that dive deeper into advanced modeling techniques and methods for using big data in R.
Offered by
Difficulty Level
Intermediate
Completion Time
25h24m
Language
English
About Book
Who Is This Book For?
This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.
Machine Learning with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 25h24m total length
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black-Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with k-means
Evaluating Model Performance
Being Successful with Machine Learning
Advanced Data Preparation
Challenging Data – Too Much, Too Little, Too Complex
Building Better Learners
Making Use of Big Data
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