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Machine Learning with R, by Brett Lantz
PDF Download Machine Learning with R, by Brett Lantz
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R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required - this book will take you methodically through every stage of applying machine learning.
Overview
- Harness the power of R for statistical computing and data science
- Use R to apply common machine learning algorithms with real-world applications
- Prepare, examine, and visualize data for analysis
- Understand how to choose between machine learning models
- Packed with clear instructions to explore, forecast, and classify data
In Detail
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.
"Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.
How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.
"Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
What you will learn from this book
- Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
- Use R to prepare data for machine learning
- Explore and visualize data with R
- Classify data using nearest neighbor methods
- Learn about Bayesian methods for classifying data
- Predict values using decision trees, rules, and support vector machines
- Forecast numeric values using linear regression
- Model data using neural networks
- Find patterns in data using association rules for market basket analysis
- Group data into clusters for segmentation
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, and “big” data
Approach
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.
- Sales Rank: #453633 in Books
- Published on: 2013-10-25
- Released on: 2013-10-25
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .90" w x 7.50" l, 1.49 pounds
- Binding: Paperback
- 396 pages
About the Author
Brett Lantz
Brett Lantz has spent the past 10 years using innovative data methods to understand human behavior. 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 interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When he's not spending time with family, following college sports, or being entertained by his dachshunds, he maintains dataspelunking.com, a website dedicated to sharing knowledge about the search for insight in data.
Most helpful customer reviews
33 of 34 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.
43 of 45 people found the following review helpful.
Less tech speak, more meaty
By Amazon Customer
First off, I am newbie to both machine learning and R and wanted find a starting point somewhere. I browsed around many books before deciding on this one. The writing style of Mr. Lantz is provided in a very understandable/readable manner. It's akin to someone sitting next to you and explaining things in a down to earth, layman's fashion rather than try to "tech speak" you to death with complicated explanations (aka formal textbook). Just the right amount of hand holding for me. I highlight quite bit and it's actually difficult with this book as there isn't much fluff. He's very succinct. The books states that it's for someone who know some ML and no R or R and no ML. I don't know either and the material is digestible except for one thing: review your stats! I took statistics long ago in college and never really learned it well the first time so I had stop and reread core concepts before continuing. Do yourself a favor and review basic statistics and probability before you start this book. I read both "Naked Statistics" and "Statistics in Plain English" and it helped me a great deal (and probably will continue to do so since it appears a bulk of machine learning is stats and prob). Currently into about a third of the way in and I am finding it to be very enjoyable and practical. Other reviewers point out that this book is too basic and this may be the case, but for someone like me who is starting from absolute scratch and who needs to understand basic ML concepts (AND basic R) I find it a great book. Will post an addendum once I complete it.
5 of 5 people found the following review helpful.
Best ML learning resource I've found
By Dim Dandy
This book is fantastic. I've been through the first four chapters, so I can't give a complete review yet, but if the rest of the book conains surprises, I will update.
Chapter 3 is on k-nearest neighbor and chapter 4 is on naive Bayes. Both provide a clear explanation of the technique, and its pros and cons, along with a simple, non-programming example. Then there is an excellent walkthrough of applying the technique to real world data, and some hints at how to improve the results by adjusting the inputs. After each chapter, I felt that I was ready to apply the technique, with the understanding that I did not yet understand everything about the topic. The chapters also are careful to explain that 1) the particular R package used has more options than have been covered and 2) there are other R packages to be investigated and experimented with.
I just couldn't be happier with these chapters, and I am fired up to keep reading and working along with the examples. It has been years since I have been reading a tech book and thinking, "OK, just a little bit more, I don't want to quit yet" instead of, "Have I spent enough time and effort on this for one day yet?" It's that good.
Although the book does provide a bit of an introduction to R, it is by no means comprehensive, and I have found that R takes time, patience and frustration to master (not that I've mastered it yet). So if I had been a complete R newbie, I probably would have struggled more.
I also found that a recent course I took in statistical inference was helpful, although not really required. At one point the book describes normalizing features using Z scores. I knew what Z scores were, so that was a point I didn't need to simply take on faith.
If you are relatively new to R and machine learning, and you want to learn to do machine learning with R, this *is* the book.
UPDATE: I am working through chapter 10, on validating model performance, and it is a goldmine. Maybe it's a matter of my general knowledge about machine learning concepts coalescing at the same time I'm reading this chapter, but it was like a long line of lightbulbs were getting switched on in my head.
UPDATE 2: I've now read the entire book and chapter 11, on improving model performance, was invaluable. It introduces random forests, and somehow finds a way to make the concept easy to comprehend, where every other article I've read or video I've watched made it seem very complicated. This chapter has given me the confidence to start tackling more sophisticated kaggle competitions using ensemble methods. Really looking forward to it.
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