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R Machine Learning Essentials - Comprehensive Guide for Data Analysis & Predictive Modeling | Perfect for Data Scientists, Researchers & AI Developers
R Machine Learning Essentials - Comprehensive Guide for Data Analysis & Predictive Modeling | Perfect for Data Scientists, Researchers & AI Developers

R Machine Learning Essentials - Comprehensive Guide for Data Analysis & Predictive Modeling | Perfect for Data Scientists, Researchers & AI Developers

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Description

Gain quick access to the machine learning concepts and practical applications using the R development environmentAbout This BookBuild machine learning algorithms using the most powerful tools in RIdentify business problems and solve them by developing effective solutionsHands-on tutorial explaining the concepts through lots of practical examples, tips and tricksWho This Book Is ForIf you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required.What You Will LearnIntroduce yourself to the basics of machine learning and RDevelop an interactive data analysis with R to get insights into the dataExplore business problems and identify key features that are highly relevant to the solutionBuild machine learning algorithms using the most powerful tools in RApply different machine learning techniques for different kinds of business problemsValidate the results of the techniques and identify the best solution to a problemIdentify business problems and solve them by developing effective solutionsIn DetailR Machine Learning Essentials provides you with an introduction to machine learning with R. Machine learning finds its applications in speech recognition, search-based operations, and artificial intelligence, among other things. You will start off by getting an introduction to what machine learning is, along with some examples to demonstrate the importance in understanding the basic ideas of machine learning. This book will then introduce you to R and you will see that it is an influential programming language that aids effective machine learning. You will learn the three steps to build an effective machine learning solution, which are exploring the data, building the solution, and validating the results. The book will demonstrate each step, highlighting their purpose and explaining techniques related to them.By the end of this book, you will be able to use the machine learning techniques effectively, identify business problems, and solve them by applying appropriate solutions.

Reviews

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- Verified Buyer
I've been using R since 2002 for a large variety of projects in marketing research, analytics and data science, but now I use it much rarely. In the recent years my data science projects as a practitioner required me to strongly concentrate on using Python.That's why, the last week, when helping a colleague of mine in a project she is running with R, I needed to quickly check for the best ways in R to do exploratory analysis, build a few models, validate and test them, report the results. And I needed some help for that myself, because I indeed couldn't remember all the details and subtleties from the R language and its libraries!Michele Usuelli's book came in great aid in that. Michele is the Lead Data Scientist at Microsoft UK and he uses R everyday for solving data science problems for a variety of different customers. In his book he concentrated all the essentials you need to run your project using R he got from his experience. As I skimmed through the book I could find everything that I needed: loading data, data exploration, feature manipulation, training of a model, tuning and validation, error measures. Everything you need for running your project is there, in an essential form but written in clear words and with ready to be used code snippets.Being an author myself (of Python books!), I much appreciated Michele's book and I personally recommend it to any practitioner in need of being able to fast set up or validate a project of her/his own or for her/his data science team.