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Machine Learning with R
Machine Learning with R

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

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Machine Learning with R

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Description

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2015
Length: 452 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784394523
Category :
Languages :

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Product Details

Publication date : Jul 31, 2015
Length: 452 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784394523
Category :
Languages :

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Table of Contents

13 Chapters
1. Introducing Machine Learning Chevron down icon Chevron up icon
2. Managing and Understanding Data Chevron down icon Chevron up icon
3. Lazy Learning – Classification Using Nearest Neighbors Chevron down icon Chevron up icon
4. Probabilistic Learning – Classification Using Naive Bayes Chevron down icon Chevron up icon
5. Divide and Conquer – Classification Using Decision Trees and Rules Chevron down icon Chevron up icon
6. Forecasting Numeric Data – Regression Methods Chevron down icon Chevron up icon
7. Black Box Methods – Neural Networks and Support Vector Machines Chevron down icon Chevron up icon
8. Finding Patterns – Market Basket Analysis Using Association Rules Chevron down icon Chevron up icon
9. Finding Groups of Data – Clustering with k-means Chevron down icon Chevron up icon
10. Evaluating Model Performance Chevron down icon Chevron up icon
11. Improving Model Performance Chevron down icon Chevron up icon
12. Specialized Machine Learning Topics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(40 Ratings)
5 star 65%
4 star 25%
3 star 5%
2 star 2.5%
1 star 2.5%
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Top Reviews

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Rob Lawton Oct 03, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
William C Scott Aug 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Cesar A. Souza Feb 07, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very good book, with everything you need to start working with machine learning and R.
Amazon Verified review Amazon
Sudheer Rao Jul 12, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very easy guide to learn the basic concept of machine learning
Amazon Verified review Amazon
Chris Papesh Sep 23, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"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.
Amazon Verified review Amazon
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