Machine Learning with R

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Packt Publishing Ltd, Jul 31, 2015 - Computers - 452 pages
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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.


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User Review  - nmarun - LibraryThing

This is a very hands-on book written in an easy-to-understand language. It kept me engaged through all the chapters with code examples on many of the machine learning algorithms. I generally don't ... Read full review

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i want to buy this book....please tell me where can i get it from?


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 kmeans
Evaluating Model Performance
Improving Model Performance
Specialized Machine Learning Topics

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About the author (2015)

Brett Lantz has spent more than 10 years using innovative data methods to understand human behavior. A trained sociologist, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, Brett has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When not spending time with family, following college sports, or being entertained by his dachshunds, he maintains, a website dedicated to sharing knowledge about the search for insight in data.

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