Machine learning with R Discover how to build machine learning algorithms, prepare data,and dig deep into data prediction techniques with R by Brett Lantz

By: Brett Lantz [Author]Publisher: Mumbai Packt 2015Edition: 2Description: xiii, 426 pages: Ilustrations; 24 cmISBN: 9781784393908Subject(s): DDC classification: 006.31
Contents:
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 Lea
Abstract: 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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General Stacks
Non-fiction 006.31 LAN (Browse shelf) Available 001058

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 Lea

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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