Book
Machine Learning with R
Brett Lantz teaches you how to uncover key insights and make new predictions with this hands-on, practical guide to machine learning with R. This third edition is for experienced R users and beginners. The book is fully updated to R 3.6, featuring newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.
Offered by
Difficulty Level
Intermediate
Completion Time
15h16m
Language
English
About Book
Who Is This Book For?
Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Machine Learning with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h16m 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
Improving Model Performance
Specialized Machine Learning Topics
Related Resources
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