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
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Summary


This chapter was about how to set up you and your team for success in any project that you tackle. The CRISP-DM process is put forward as a flexible and comprehensive framework in order to facilitate the softer skills of communication and influence. Each step of the process and the tasks in each step were enumerated. More than that, the commentary provides some techniques and considerations to with the process execution. By taking heed of the process, you can indeed become an agent of positive change to any organization.

The other item put forth in this chapter was an algorithm flowchart; a cheat sheet to help identify some of the proper techniques to apply in order to solve the business problem. With this foundation in place, we can now move on to applying these techniques to real-world problems.