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R Machine Learning By Example

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R Machine Learning By Example

Autor: Raghav Bali, Dipanjan Sarkar
Broj strana: 340
ISBN broj: 9781784390846
Godina izdanja: 2016.

Pregleda (30 dana / ukupno): 24 / 1910

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What You Will Learn

  • Utilize the power of R to handle data extraction, manipulation, and exploration techniques
  • Use R to visualize data spread across multiple dimensions and extract useful features
  • Explore the underlying mathematical and logical concepts that drive machine learning algorithms
  • Dive deep into the world of analytics to predict situations correctly
  • Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
  • Write reusable code and build complete machine learning systems from the ground up
  • Solve interesting real-world problems using machine learning and R as the journey unfolds
  • Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science

Book Description

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 making machine learning give them data-driven insights to grow their business. 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.

You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.

Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.


Raghav Bali

Raghav Bali has a master's degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies in the world. Raghav is a shutterbug, capturing moments when he isn't busy solving problems.

Dipanjan Sarkar

Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He received his master's degree in information technology from the International Institute of Information Technology, Bangalore. His areas of specialization includes software engineering, data science, machine learning, and text analytics.

Dipanjan's interests include learning about new technology, disruptive start-ups, and data science. In his spare time, he loves reading, playing games, and watching popular sitcoms. He has also reviewed Data Analysis with R, Learning R for Geospatial Analysis, and R Data Analysis Cookbook, all by Packt Publishing.

Table of Contents

Chapter 1: Getting Started with R and Machine Learning
Chapter 2: Let's Help Machines Learn
Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis
Chapter 4: Building a Product Recommendation System
Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics
Chapter 6: Credit Risk Detection and Prediction – Predictive Analytics
Chapter 7: Social Media Analysis – Analyzing Twitter Data
Chapter 8: Sentiment Analysis of Twitter Data


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