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Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners
Finally, A Blueprint for Machine Learning with R! Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE This book uses plain language rather than a ton of equations; I’m assuming you never did like linear algebra, don’t want to see things derived, dislike complicated computer code, and you’re here because you want to try successful machine learning algorithms for yourself. YOUR PERSONAL BLUE Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data. THIS BOOK IS FOR YOU IF YOU Focus on explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. YOU'LL LEARN HOW Unleash the power of Decision Trees. Develop hands on skills using k-Nearest Neighbors. Design successful applications with Naive Bayes. Deploy Linear Discriminant Analysis. Explore Support Vector Machines. Master Linear and logistic regression. Create solutions with Random Forests. Solve complex problems with Boosting. Gain deep insights via K-Means clustering. Acquire tips to enhance model performance. For each machine learning algorithm, every 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. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Machine Learning Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!
Kindle Edition
Published May 17, 2017
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August 5, 2018
I was pleasantly surprised by this book. Yes, it has some, extra, commas, and a shortage of copy, editing, but it's quite comprehensible. The book is a quick read; my primary concern is that the explanations are for the most part simply too terse, and that a novice will have trouble following them.
I like the author's way of thinking. For each model, he makes a point of talking about which theoretical assumptions are important and which can be flagrantly violated while still getting good results. He also gives a presentation of how to interpret models; in some cases, such as LDA, this is quite a bit better than other treatments I've read. He provides an intuitive English explanation of what a model is doing without much or any attempt to explain the math or the implementation, which can be more useful than a mathematical explanation that is too difficult or time-consuming to follow. Each chapter also includes references to 2-3 papers where the model performed well on a real problem.
The book's great weakness is that its assumptions about incoming knowledge create a very narrow target audience for it. Although the book explains the modeling algorithms, it assumes you already know how to use R and it assumes you are already familiar with a wide variety of statistical concepts. Ideas like "within-class covariance matrix", "recall", and "ROC curve" are used without explanation; some are explained much later and some never. In the first chapter, concepts are explained by analogy to linear regression, but the latter is not discussed in this book until chapter 6. I would have much preferred that an introductory book explain these ancillary ideas as well and be more careful about the sequence of presentation.
Another drawback is that the price seems to have doubled since I bought it - I guess the author decided it was an inelastic market! ;)
For most people, I would continue to recommend An Introduction to Statistical Learning: With Applications in R as the best introductory book. I would recommend Lewis' book if you are already somewhat statistically informed and want a quick tour of applied models, or if you've already read other books and want an alternative perspective; his coverage of LDA and the selection of clusters in K Means are superior to that in ISLR.
I like the author's way of thinking. For each model, he makes a point of talking about which theoretical assumptions are important and which can be flagrantly violated while still getting good results. He also gives a presentation of how to interpret models; in some cases, such as LDA, this is quite a bit better than other treatments I've read. He provides an intuitive English explanation of what a model is doing without much or any attempt to explain the math or the implementation, which can be more useful than a mathematical explanation that is too difficult or time-consuming to follow. Each chapter also includes references to 2-3 papers where the model performed well on a real problem.
The book's great weakness is that its assumptions about incoming knowledge create a very narrow target audience for it. Although the book explains the modeling algorithms, it assumes you already know how to use R and it assumes you are already familiar with a wide variety of statistical concepts. Ideas like "within-class covariance matrix", "recall", and "ROC curve" are used without explanation; some are explained much later and some never. In the first chapter, concepts are explained by analogy to linear regression, but the latter is not discussed in this book until chapter 6. I would have much preferred that an introductory book explain these ancillary ideas as well and be more careful about the sequence of presentation.
Another drawback is that the price seems to have doubled since I bought it - I guess the author decided it was an inelastic market! ;)
For most people, I would continue to recommend An Introduction to Statistical Learning: With Applications in R as the best introductory book. I would recommend Lewis' book if you are already somewhat statistically informed and want a quick tour of applied models, or if you've already read other books and want an alternative perspective; his coverage of LDA and the selection of clusters in K Means are superior to that in ISLR.
Displaying 1 of 1 review