Machine Learning with R - Third Edition - by Brett Lantz (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>Brett Lantz teaches you how to uncover key insights and make new predictions with this hands-on, practical guide to machine learning with R. This third edition is for experienced R users and beginners. The book is fully updated to R 3.6, featuring newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><strong>Solve real-world data problems with R and machine learning</strong></p><p><strong>Key Features</strong></p> <ul> <li>Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond</li> <li>Harness the power of R to build flexible, effective, and transparent machine learning models</li> <li>Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz</li> </ul> <p><strong>Book Description</strong></p> <p>Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.</p> <p>Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.</p> <p>This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.</p> <p><strong>What you will learn</strong></p> <ul> <li>Discover the origins of machine learning and how exactly a computer learns by example</li> <li>Prepare your data for machine learning work with the R programming language</li> <li>Classify important outcomes using nearest neighbor and Bayesian methods</li> <li>Predict future events using decision trees, rules, and support vector machines</li> <li>Forecast numeric data and estimate financial values using regression methods</li> <li>Model complex processes with artificial neural networks -- the basis of deep learning</li> <li>Avoid bias in machine learning models</li> <li>Evaluate your models and improve their performance</li> <li>Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow</li> </ul> <p><strong>Who this book is for</strong></p> <p>Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.</p>
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