Applied Unsupervised Learning with R

▶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

▶Key Features
⦁ 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

▶Who This Book Is 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.

▶Description
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all the features of R that enable you to understand your data better and get answers to all your business questions.

▶Audience
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.

▶Approach
Applied Unsupervised Learning with R takes a hands-on approach to using R to reveal the hidden patterns in your unstructured data. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.