Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition

Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition

<p><b>Solve real-world data problems with R and machine learning</b></p> <h4>Key Features</h4> <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> <h4>Book Description</h4> <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> <h4>What you will learn</h4> <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> <h4>Who this book is for</h4> <p>Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.</p><h4>Table of Contents</h4> <ol> <li>Introducing Machine Learning</li> <li>Managing and Understanding Data</li> <li>Lazy Learning – Classification Using Nearest Neighbors</li> <li>Probabilistic Learning – Classification Using Naive Bayes</li> <li>Divide and Conquer – Classification Using Decision Trees and Rules</li> <li>Forecasting Numeric Data – Regression Methods</li> <li>Black Box Methods – Neural Networks and Support Vector Machines</li> <li>Finding Patterns – Market Basket Analysis Using Association Rules</li> <li>Finding Groups of Data – Clustering with k-means</li> <li>Evaluating Model Performance</li> <li>Improving Model Performance</li> <li>Specialized Machine Learning Topics</li> </ol>