Machine Learning with R
This second edition provides focused and practical knowledge to help you to build algorithms and crunch your data. You'll learn how to apply machine learning methods to deal with common tasks, discover the analytical tools that you need to gain insights from complex data, and choose the correct algorithm for your specific needs.
Offered by
Difficulty Level
Intermediate
Completion Time
15h4m
Language
English
About Book
Who Is This Book For?
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Machine Learning with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h4m total length
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning: Classification using Nearest Neighbors
Probabilistic Learning: Classification using Naïve Bayes
Divide and Conquer: Classification using 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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