Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
About the Author
About the Reviewers
Packt Upsell
Customer Feedback

Chapter 16. Sources

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Hinton, G.E., Salakhutdinov, R.R., (2006), Reducing the Dimensionality of Data with Neural Networks, Science, August 2006, 313(5786):504-7

James, G., Witten, D., Hastie, T., Tisbshirani, R. (2013), An Introduction to Statistical Learning, 1st ed. New York: Springer

Kodra, E., (2011), Exploring Granger Causality Between Global Average Observed Time Series of Carbon Dioxide and Temperature, Theoretical and Applied Climatology, Vol. 104 (3), 325-335

Natekin, A., Knoll, A., (2013),  Gradient Boosting Machines, a Tutorial, Frontiers in Neurorobotics, 2013; 7-21.

Tibshirani, R., (1996), Regression Shrinkage and Selection...