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R Machine Learning By Example

You're reading from R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

Product type Paperback

Published in Mar 2016

Publisher

ISBN-13 9781784390846

Length 340 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Author (1):

Raghav Bali

View More author details

Table of Contents (10) Chapters

Preface

1. Getting Started with R and Machine Learning FREE CHAPTER

2. Let's Help Machines Learn

3. Predicting Customer Shopping Trends with Market Basket Analysis

4. Building a Product Recommendation System

5. Credit Risk Detection and Prediction – Descriptive Analytics

6. Credit Risk Detection and Prediction – Predictive Analytics

7. Social Media Analysis – Analyzing Twitter Data

8. Sentiment Analysis of Twitter Data

Index

Preface

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them data-driven insights to grow their businesses. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.

This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.

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Authors (1)

Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

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