• Explore the social media APIs in R to capture data and tame it
• Employ the machine learning capabilities of R to gain optimal business value
• A hands-on guide with...

• Get to grips with the application of Machine Learning methods using an extensive set of R packages
• Understand the benefits and potential pitfalls of using machine learning methods
...

• Understand the principles of Bayesian Inference with less mathematical equations
• Learn state-of-the art Machine Learning methods
• Familiarize...

Inverse limits provide a powerful tool for constructing complicated spaces from simple ones. They also turn the study of a dynamical system consisting of a space and a self-map into a study of a (likely more complicated) space and a self-homeomorphism. In four chapters along with an appendix containing background material the authors...

The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data...

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important...

The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to help your...

Bridging the gap between the development and history of real analysis, Introduction to Real Analysis: An Educational Approach presents a comprehensive introduction to real analysis while also offering a survey of the field. With its...

This book is the revised and extended second edition of Statistics for Linguistics with R. The comprehensive revision includes new small sections on programming topics that facilitate statistical analysis, the addition of a variety of statistical functions readers can apply to their own data, and a revision of overview sections on statistical...