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R Machine Learning By Example
R Machine Learning By Example

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

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R Machine Learning By Example

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Key benefits

  • Get to grips with the concepts of machine learning through exciting real-world examples
  • Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning
  • Learn to build your own machine learning system with this example-based practical guide

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.

Who is this book for?

If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 31, 2016
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392635
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Languages :
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What do you get with eBook?

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Product Details

Publication date : Mar 31, 2016
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392635
Category :
Languages :
Tools :

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Frequently bought together


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Table of Contents

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

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(14 Ratings)
5 star 78.6%
4 star 14.3%
3 star 0%
2 star 0%
1 star 7.1%
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Top Reviews

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Ankita Apr 28, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Written in simple language easy to understand, but you need to have some background knowledge of machine learning before starting with this book
Amazon Verified review Amazon
Amazon Customer May 18, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Really diverse examples of data science problems have been covered in this book ranging from text analytics to classification and pattern detection. Overall a great book!
Amazon Verified review Amazon
Ajitava May 20, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is quite good if you are getting started with either R or Machine Learning and the case-study based approach really helps in understanding how to use machine learning in various areas.
Amazon Verified review Amazon
Amazon Customer May 17, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Background: Data Scientist in the financial spaceOverall Thoughts: This was one of the few books I saw which had working implementations of solutions to the various practical examples in the books. Often I have found non-working code in several books which authors didn't bother correcting. Really loved the fraud detection & prediction and the recommendation system problems.
Amazon Verified review Amazon
Amazon Customer Jun 06, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book was exactly what I was looking for...The content starts from the very basic, something as simple as the basics of R and then builds on content as we go about solving real world challenges. The code base is available online and the book helps us understand the intricate details of the importance of each line of code. The presentation is quite structured and is a great starting point for someone who is passionate to use R for machine learning. Particularly, the five-six real world problems explained in such a lucid and impactful way was the most striking aspect for me!A much recommended book in your library to sail through the basics of R in the machine learning world!"
Amazon Verified review Amazon
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