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Machine Learning with R - Second Edition - Deliver Data Insights with R and Predictive Analytics, by Brett Lantz
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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
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 Râa cross-platform, zero-cost statistical programming environmentâthere 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 impact, and have constantly developing insights? R gives you access to the cutting-edge power you need 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 youâll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, youâll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.
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 neighbour 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
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 while 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- Sales Rank: #150049 in eBooks
- Published on: 2015-07-31
- Released on: 2015-07-31
- Format: Kindle eBook
About the Author
Brett Lantz
Brett Lantz has spent more than 10 years using innovative data methods to understand human behavior. A trained sociologist, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, Brett has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When not spending time with family, following college sports, or being entertained by his dachshunds, he maintains http://dataspelunking.com/, a website dedicated to sharing knowledge about the search for insight in data.
Most helpful customer reviews
25 of 25 people found the following review helpful.
There are some useful gems in this book
By Philip H.
I'm torn. There are some useful gems in this book, and for the most part, the presentation is simple, albeit a bit pedantic and cartoonish at times. If I was trying to get up to snuff on a new machine learning method, I might start here, since it *does* provide starter code for a variety of problems. That's quite handy. It doesn't, however, go into much depth at all on any one topic. You can't read this book and expect to know how to do any one of these methods well. Certainly, it's a tall order to ask any one book to cover all ML topics in depth, but any potential reader should be aware that this just skims the surface of a whole bunch of topics.
On top of this, who in the world edited this book? Every other page has horrible typos, missing words, repeated sentences. These are not trivial errors either. This is a book about data analysis and yet the reported data are clearly wrong in places, e.g., a result is listed as .06 percent in one spot and then .0006 in another (p. 271). Basic subject-verb agreement errors riddle the text, e.g. "These output is shown as follows". Sometimes these are trivial errors, but other times you have absolutely no idea what the intended meaning is. I have about 100 pages more to read but I'm starting to wonder if I'm just wasting my time.
6 of 6 people found the following review helpful.
A good grounding in data mining concepts, and how to apply them in R
By Rob Lawton
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.
6 of 6 people found the following review helpful.
Data analysis for action
By Eran Raviv
Excellent overview for the different classical ways of machine learning. The writer clearly knows his way around. Explanations are down-to-earth, light on the math and theory but with references when they are needed. Glossing over the theory enables the author to condense a lot of information into those ~350 pages. Heavy on practical advice and good practices.Good accounts of the different algorithms are of course available online, but the big advantage of this book in my opinion is its eye for the end-game. What actionable insights can you extract from your dataset and how, using clear examples accompanied with R code. A book much appreciated.
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