Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Arrow left icon
Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

R


R is a language for data analysis and is used as an environment that is a primary driver in the field of Machine learning, statistical computing, and data mining and provides a comprehensive platform for basic and advanced visualizations or graphics. Today, R is a basic skill that almost all data scientists or would-be data scientists have or must learn.

R is primarily a GNU project known to be similar to the S language that was initially developed at Bell Laboratories (formerly known as AT&T and now, Lucent Technologies) by John Chambers and team. The initial goal for S was to support all statistical functions and was widely used by hard-core statisticians.

R comes with a wide range of open source packages that can be downloaded and configured free of cost, and are installed or loaded on a need basis into the R environment. These packages provide out-of-box support for a wide variety of statistical techniques that include linear and non-linear modeling, time-series analysis, classification...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime