Packt is pleased to announce the release of Machine Learning with R, a book packed with clear instructions to explore, forecast, and classify data. This book will help developers harness the power of R for statistical computing and data science. The print book comes in at 396 pages and is competitively priced at $54.99, whilst the eBook version is available for $28.04 in all the popular formats.
About the author:
Brett Lantz is a sociologist by training and has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. He has spent the past 10 years using innovative data methods to understand human behaviour. His love of machine learning began while studying a large database of teenagers' social networking website profiles.
R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Polls and surveys of data miners show that R's popularity has increased substantially in recent years.
Machine Learning with R is a practical tutorial that uses hands-on examples to take readers through a real-world application of machine learning. Readers will explore machine learning with R using clear and practical examples while being provided with the necessary technical details at the same time. The book is a perfect fit for both machine learning beginners and those with experience.
Machine Learning with R will provide readers with the analytical tools needed to quickly gain insights from complex data. Readers will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, readers will gain hands-on experience that will transform the way they think about data.
The book covers the following essential topics:
Chapter 1: Introducing Machine Learning
Chapter 2: Managing and Understanding Data
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
Chapter 6: Forecasting Numeric Data – Regression Methods
Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
Chapter 9: Finding Groups of Data – Clustering with k-means
Chapter 10: Evaluating Model Performance
Chapter 11: Improving Model Performance
Chapter 12: Specialized Machine Learning Topics
Packt Publishing has also released the following books on R :
• Big Data Analytics with R and Hadoop
• Introduction to R for Quantitative Finance
• R Statistical Application Development by Example Beginner's Guide
Packt is one of the most prolific and fastest-growing tech book publishers in the world. Originally focused on open source software, Packt books now focus on practicality, recognizing that readers are ultimately concerned with getting the job done. Packt's digitally-focused business model allows them to publish up-to-date books in very specific areas.
|Machine Learning with R|
|Clear instructions to explore, forecast, and classify data.
For more information, please visit: http://www.packtpub.com/machine-learning-with-r/book