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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.

Who is this book for?

Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

What you will learn

Harness the power of R to build common machine learning algorithms with realworld data science applications

Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results

Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems

Classify your data with Bayesian and nearest neighbour methods

Predict values 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, and big data

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Amazon verified reviews

Rob Lawton Oct 03, 2015

I am pleased to have bought this book (directly from Packt, the publisher) based on positive reviews of the first edition. My background is as a SQL programmer and CRM data analyst, and although I had some experience of data mining algorithms in other software, I do not have a lot of prior experience in R.Before jumping into descriptions of the various data mining algorithms, there is some useful material on the basics of data handling in R, which was a useful refresher for me as an R novice.After this, the author describes clearly and concisely the use of the various algorithms, together with discussion on the strengths and weaknesses of each. There are examples given, using mostly real world data (which is available to download). These are easy to follow, giving enough detail to understand the concepts without getting bogged down in too much statistical detail.I found it useful to have some understanding of the concepts of some of the mining models, but this is not essential as the book gives a good grounding in both the concepts, and how to apply them in R.

Amazon Verified review

William C Scott Aug 05, 2018

Amazing book. I can't recommend it enough. Within a day or so, you can be running a Machine Learning algorithm on some data. Lantz provides examples of several different approaches in ML (KNN, SVM, RNN, etc). And then depending on your problem, you can start playing around with the packages.

Cesar A. Souza Feb 07, 2019

This is a very good book, with everything you need to start working with machine learning and R.

Sudheer Rao Jul 12, 2016

Very easy guide to learn the basic concept of machine learning

Chris Papesh Sep 23, 2015

"Machine Learning with R-Second Edition" is an excellent introduction to building algorithms using R. Bert Lantz, the author, provides hands-on case studies combined with essential theory to understand basic to advanced methods. I served as an Assistant Vice President for Carnegie Mellon University and a Director with Oracle and PeopleSoft; I am confident that "Machine Learning with R" will provide a strong framework for new students, but also an excellent guide for working professionals learning how to use R. I found that the example code worked perfectly and that I could quickly build the case study models.My firm, Pacific Health Software-PHS, is building Open Source free Predictive Analytic (PA) software with PA models built upon Open Source clinical systems: OpenEMR; OpenMRS & VISTA. Initial free models (built in R) with links to OpenEMR clinical data-sets will be released in October 2015; http://phs-redmine.bitnamiapp.com/redmine/.We anticipate that models can bring higher than 90% to 98% prediction rates on the course to take with major diseases such as Heart Disease.We plan to work with the US Peace Corps to assist clinical decision making in Peace Corp clinics in Africa and Asia that use OpenEMR clinical systems; we will be asking a community of volunteers to build and distribute additional free PA models. We will recommend that community volunteers, developers, who are new to using R, buy "Machine Learning with R": to learn to build new models with R.

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

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