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Mastering Machine Learning with R, by Cory Lesmeister
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Master machine learning techniques with R to deliver insights for complex projects
About This Book- Get to grips with the application of Machine Learning methods using an extensive set of R packages
- Understand the benefits and potential pitfalls of using machine learning methods
- Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system
If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.
What You Will Learn- Gain deep insights to learn the applications of machine learning tools to the industry
- Manipulate data in R efficiently to prepare it for analysis
- Master the skill of recognizing techniques for effective visualization of data
- Understand why and how to create test and training data sets for analysis
- Familiarize yourself with fundamental learning methods such as linear and logistic regression
- Comprehend advanced learning methods such as support vector machines
- Realize why and how to apply unsupervised learning methods
Machine learning is a field of Artificial Intelligence to build systems that learn from data. 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 to your data.
The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series.
The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.
Style and approachThis is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.
- Sales Rank: #1163477 in eBooks
- Published on: 2015-10-28
- Released on: 2015-10-28
- Format: Kindle eBook
About the Author
Cory Lesmeister
Cory Lesmeister currently works as an advanced analytics consultant for Clarity Solution Group, where he applies the methods in this book to solve complex problems and provide actionable insights. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. A former U.S. Army Reservist, Cory was in Baghdad, Iraq, in 2009 as a strategic advisor to the 29,000-person Iraqi oil police, where he supplied equipment to help the country secure and protect its oil infrastructure. An aviation aficionado, Cory has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license. Cory lives in Carmel, IN, with his wife and their two teenage daughters.
Most helpful customer reviews
9 of 11 people found the following review helpful.
Poor
By Dimitri Shvorob
Even a dog can publish a book with Packt: the publisher operates a no-editors, no-graphic-designers, no-standards business model and publishes anything by anybody, hoping that something sticks. In the hot "data science / machine learning" segment, Packt met 2015 with a bang, churning out the god-awful "Learning Data Mining with R" by Makhabel and "R for Data Science" by Toomey. Then four more titles came out:
"Mastering Predictive Analytics with R" by Forte, $45
"Mastering Machine Learning with R" by Lesmeister, $40
"R Data Analysis Cookbook" by Viswanathan and Viswanathan, $40
"Machine Learning with R Cookbook" by Yu-Wei, $40
I checked them out and, long story short, "Mastering Machine Learning with R" is the stinker of the bunch. It is not as bad as Makhabel's or Toomey's, but, like those two, it is a low-quality, unoriginal book by an author with rudimentary, informal statistics/machine-learning education, regurgitating something he had read in a better book. You just don't have to settle for this - even sticking to Packt's catalog, the books by Forte and Lantz are two far superior options.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
0 of 0 people found the following review helpful.
Great Introduction to ML with R
By HDFS_Python
Overall, I think the book was good and I enjoyed reading it, for a statistics book this is a praise. The following pros will seem lacking to the cons but believe me that it is because the book was overall good and any compliment hits nearly all chapters in the book. When I did see a con, I expanded on it to give full insight into the issue. As in any endeavor of this sort, it is always a challenge to find the right balance between theory and application.
Pros:
The book contains companion code. This means a student can save the code for the future, load it in when necessary, and alter the code to learn from it. In my honest opinion, this is the best option for me to study and learn a topic. Each chapter covers a different over-arching problem, which is gradually solved when new-techniques and strategies are introduced then implemented to solidify the knowledge with use. Allowing the reader to see what scenarios the technique surrounds and how it is run. The book covers a wide variety of topics, allowing a student to become a jack-of-all-trades, in the use of machine learning and advanced statistical techniques in R.
Cons:
I believe this book is suited well for someone with a mathematical and programming background. Without either, the book would seem challenging and daunting in some areas (i.e. Neural Networks). The book would not be impossible for someone without knowledge in R to read it, but it would be advised that the person knows passing knowledge of the software before they begin this book.
Lack of mathematical theory. In a few areas, the book shows how to use the topic to reach the end but does not include the deep mathematical background into how the calculation are run. It has a chance of creating a black-box scenario where someone knows how it works on the outside without a clue of how it is run on the inside. In my opinion, this isn’t always necessary knowing how to calculate acf, pacf, and eacf by hand is nice but doesn’t help when running acf(model). Side note: no reasonable person would calculate acf past five lag or pacf by hand.
Overview for all subjects. The way the book was made for ease in learning makes brings up a small problem. Some challenging data sets may exceed the scope of the books training material and could lead to the reader being ill prepared. An example of this problem would be if a time series problem contains innovative or additive outlier. This means the student may receive a model with the lowest AIC value, but the formula may not be the most optimized format. For this case, a student should know when a problem is showing intriguing characteristics and should being a research process into how to confront these problems, through other reading material, internet, or professional network.
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