Machine Learning with R
β Abhijit Ghatak
π Library
π
2017
π Springer
π English
β¦ LIBER β¦
π
Machine Learning with R
β Scribed by Brett Lantz
- Publisher
- Packt Publishing
- Year
- 2015
- Tongue
- English
- Leaves
- 452
- Series
- Community experience distilled
- Edition
- 2
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
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
β’ Harness the power of R to build common machine learning algorithms with real-world data science applications
β’ Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
β’ Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
β’ Classify your data with Bayesian and nearest neighbor methods
β’ Predict values by using R to build decision trees, rules, and support vector machines
β’ Forecast numeric values with linear regression, and model your data with neural networks
β’ Evaluate and improve the performance of machine learning models
β’ Learn specialized machine learning techniques for text mining, social network data, big data, and more
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Machine Learning with R
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π Library
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2017
π Springer Singapore
π English
<p><p>This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and itβs applications to machine learni
Machine Learning with R
β Lantz, Brett
π Library
π
2015
π Packt Publishing
π English
Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with RAbout This Book Harness the power of R for statistical computing and data science Explore, forecast, and classify data with R Use R to apply common machine learning algorithms to real-