Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
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.
What You Will Learn
Harness the power of R to build common machine learning algorithms with real-world data science applications
Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Classify your data with Bayesian and nearest neighbor methods
Predict values by using R to build decision trees, rules, and support vector machines
Forecast numeric values with linear regression, and model your data with neural networks
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, big data, and more
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