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Applied Unsupervised Learning with R
Applied Unsupervised Learning with R

Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

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Profile Icon Alok Malik Profile Icon Bradford Tuckfield
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$26.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (10 Ratings)
eBook Mar 2019 320 pages 1st Edition
eBook
$26.99
Paperback
$38.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Alok Malik Profile Icon Bradford Tuckfield
Arrow right icon
$26.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (10 Ratings)
eBook Mar 2019 320 pages 1st Edition
eBook
$26.99
Paperback
$38.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$26.99
Paperback
$38.99
Subscription
Free Trial
Renews at $12.99p/m

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Applied Unsupervised Learning with R

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Key benefits

  • Build state-of-the-art algorithms that can solve your business' problems
  • Learn how to find hidden patterns in your data
  • Revise key concepts with hands-on exercises using real-world datasets

Description

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Who is this book for?

Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

What you will learn

  • Implement clustering methods such as k-means, agglomerative, and divisive
  • Write code in R to analyze market segmentation and consumer behavior
  • Estimate distribution and probabilities of different outcomes
  • Implement dimension reduction using principal component analysis
  • Apply anomaly detection methods to identify fraud
  • Design algorithms with R and learn how to edit or improve code

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 27, 2019
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781789951462
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Product Details

Publication date : Mar 27, 2019
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781789951462
Category :
Languages :

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Table of Contents

6 Chapters
Introduction to Clustering Methods Chevron down icon Chevron up icon
Advanced Clustering Methods Chevron down icon Chevron up icon
Probability Distributions Chevron down icon Chevron up icon
Dimension Reduction Chevron down icon Chevron up icon
Data Comparison Methods Chevron down icon Chevron up icon
Anomaly Detection 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 4.8
(10 Ratings)
5 star 80%
4 star 20%
3 star 0%
2 star 0%
1 star 0%
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Catherine Apr 28, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is very helpful in understanding the important unsupervised learning methods, such as different clustering methods, PCA. It provides great examples to use R to solve problems. Excellent for beginner to follow and to be able to use R to do the analysis. The examples are all business questions, making it easier to relate to my business questions. In addition to that, this book explains the theory behind the algorithms in languages that are easy to understand. Overall, it is an amazing book for me to study and apply unsupervised learning algorithms!
Amazon Verified review Amazon
Don Mar 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book covers a broad range of topics, including the most common unsupervised learning ideas like clustering, as well as other ideas that are more rarely discussed. The code is useful and easy to understand. I particularly liked the introduction of silhouette scores and other methods for determining how many clusters to use in k-means clustering.
Amazon Verified review Amazon
Xin Chen May 15, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I would adopt this as a textbook if I teach a class on unsupervised learning.
Amazon Verified review Amazon
Tanguy DESCHUYTENEER Feb 04, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Lotlamoreng Mosiane Dec 05, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
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