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Book Overview & Buying
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Table Of Contents
Machine Learning with R - Second Edition
By :
Machine Learning with R
By:
Overview of this book
Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.
With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.
Table of Contents (14 chapters)
Preface
Free Chapter
1. Introducing Machine Learning
2. Managing and Understanding Data
3. Lazy Learning – Classification Using Nearest Neighbors
4. Probabilistic Learning – Classification Using Naive Bayes
5. Divide and Conquer – Classification Using Decision Trees and Rules
6. Forecasting Numeric Data – Regression Methods
7. Black Box Methods – Neural Networks and Support Vector Machines
8. Finding Patterns – Market Basket Analysis Using Association Rules
9. Finding Groups of Data – Clustering with k-means
10. Evaluating Model Performance
11. Improving Model Performance
12. Specialized Machine Learning Topics
Index
