Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners
English | 7 May 2017 | ISBN: 1546483756 | 356 Pages | PDF | 8.39 MB
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 REQUIRED: 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 PRINT: 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 WANT:
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 SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. to:
YOU'LL LEARN HOW TO:
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.
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