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Description

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.

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

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Amazon verified reviews

Fabien Deneuville Aug 04, 2016

J'ai apprécié ce livre. Il est globalement très bien, fait, donne de multiples exemples. Il s'adresse à qui connaît déjà bien R et a de solides bases en analytics. Il permettra d'aller plus loin sur le machine learning, les différents types d'algorithmes, les techniques existantes... J'ai appris des choses avec ce livre.

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HDFS_Python Jun 11, 2016

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.

meitzmann Oct 20, 2016

Mastering Machine Learning in R provides a great introduction to machine learning and data analysis techniques. It is refreshing to read a statistics/data focused book that is written in an accessible manner by someone with good communication skills.The concepts are laid out in a logical format that includes Data Preparation and Business Cases, two things that are often left out of many similar texts. The author goes into detail on concepts that need it and avoids it on concepts that don’t while still providing enough resources for the reader.The code that comes with the book makes it a great resource for students or someone who is looking to teach themselves. Overall I highly recommend this text.

Mugdha Hota Oct 18, 2018

Nick P Jan 31, 2018

Great introductory book on the subject and no need for me to fumble through other books.

Mastering Machine Learning with R: Master machine learning techniques with R to deliver insights for complex projects

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