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Machine Learning with R – Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems

Amazon.com Price:  $49.42 (as of 14/04/2019 21:27 PST- Details)

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Description

Key Features

  • 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-world scenarios

Book Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R’s cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data.

Want to turn your data into actionable knowledge, predict outcomes that make real have an effect on, and have constantly developing insights? R gives you access to the state of the art power you want to master exceptional machine learning techniques.

Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R 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 can discover all the analytical tools you want to gain insights from complex data and learn how to to select the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you can learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Become the way you think about data; discover machine learning with R.

What you are going to 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 neighbour methods
  • Predict values by using R to build decision trees, rules, and Give a boost to vector machines
  • Forecast numeric values with linear regression, and model your data with neural networks
  • Evaluate and reinforce the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, big data, and more

About the Author

Brett Lantz has used innovative data methods to understand human behavior for more than 10 years. A sociologist by training, he was first enchanted by machine learning at the same time as studying a large database of teenagers’ social networking website profiles. Since then, he has worked on the interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.

Table of 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 Give a boost to 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

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