R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years. As of May 2018, R ranks 11th in the TIOBE index, a measure of popularity of programming languages. Source:wiki
R is an intense platform for data analysis and machine learning. The reason is the a lot of effective algorithms accessible, all on the one platform.
In this post I need to call attention to a few resources you can use to begin in R for Machine Learning.
The criteria I used to Select these Best Books are:
All-Round Creation, Very Practical, Generative, Affordable, Authorship, Highest Reviewed, Stimulates the mind & imagination, Practical Problems, Career Opportunities, Highest Rated, Can be read over & over, Best Sellers, Newest, Without Bias.
OK Let’s Get Started!
Top & Best Books for Machine Learning with R
1.) R in a Nutshell
If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment.
You’ll learn how to write R functions and use R packages to help you prepare, visualize and analyze data.
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2.) Hands-on Programming with R
Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.
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3.) R Cookbook by Paul Teetor
With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work but its structure can be difficult to master.
This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics and linear regression.
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4.) Advanced R by Hadley Wickham
Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.
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5.) Machine Learning with R
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.
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6.) Mastering Machine Learning with R
This book covers insights in complex projects – Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST. – Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning. – Implement advanced concepts in machine learning with this example-rich guide.
Who This Book Is For? This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field.
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7.) Data Mining with R: Learning with Case Studies
This book uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts.
The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R.
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7.) Data Mining and Business Analytics with R
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper–undergraduate level courses in data mining, predictive analytics, and business analytics.
This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
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8.) Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment.
The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.
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8.) An Introduction to Statistical Learning: with Applications in R
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented.
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9.) Practical Data Science with R
Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence and decision support.
It shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.
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10.) Learning RStudio For R Statistical Computing by Mark P.J.van der Loo
Data is coming at us faster, dirtier, and at an ever increasing rate. The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. This book will show you how to deal with it like never before, thus providing an edge and improving productivity.
“Learning RStudio for R Statistical Computing” will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics.
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FINAL WORDS
We have covered 10 popular machine learning books that attention on utilizing the R platform.
The best counsel I can offer is to pick one and read it. Read it cover to cover, take notes and do the activities. Like programming, utilizing R is a key skill that you can just form by learning and practicing.
PS: Start Understanding these books One by One. Perhaps, a $8-10 book can change your Career for
time everlasting.
Books changed my life in recent years. I was forced to peruse books in my formal education and avoided from perusing books in my corporate activity.
The above book list are some of most loved books that I would recommend you to enhance your career, a better person and a better individual.
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