Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.
- 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
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.
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
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.
Table of Contents
- Introduction to Clustering Methods
- Advanced Clustering Methods
- Probability Distributions
- Dimension Reduction
- Data Comparison Methods
- Anomaly Detection
R Tutorial: Unsupervised Learning in R | Intro Want to learn more? Take the full course at unsupervised
at your own pace.R-Session 10 - Statistical Learning - Unsupervised Learning Reference: (Book) An Introduction to Statistical Learning
with Applications in R
(Gareth James, Daniela Witten, Trevor Hastie, ...Machine Learning with R | Machine Learning Algorithms | Data Science Training | Edureka Data Science Training: -r
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, like ...Machine Learning in R - Supervised vs. Unsupervised Learn the basics of Machine Learning with R
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Course for free: ...Applied Machine Learning and Deep Learning with R : K-Means Clustering | packtpub.com This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and ...Hierarchical Clustering in R | Unsupervised Learning | Machine Learning In this video you will learn
about how to build a Hierarchical clustering
model using R
. In contrast to the K-means clustering
in ...Machine Learning in R: Building a Linear Regression Model In this machine learning
tutorial video, I will go over the steps on how you can build a simple linear regression model using ...Machine Learning with R and TensorFlow J.J. Allaire's keynote at rstudio::conf 2018 on the R
interface to TensorFlow (), a suite of packages ...Machine Learning in R: Building a Classification Model In this video, I cover the concepts and practical aspects of building a classification model using the R
programming language; ...K-Means Cluster Analysis in R Recap WSS Plot :- .Deep Learning with R for Beginners Deep learning (also known as deep structured learning) is part of a broader family of machine learning
methods based on artificial ...k-Means and Hierarchial Clustering using R-Studio In this video, we demonstrate how to perform k-Means and Hierarchial Clustering
-Studio.Machine Learning with R Tutorial: Review of k-means clustering Make sure to like & comment if you enjoy this video! This is the final video for chapter 1 of our course Unsupervised Learning
...Introduction to Cluster Analysis with R - an Example Provides illustration of doing cluster analysis with R
File: GitHub: ...Intro to Machine Learning with R & caret The R
programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity ...Applied Machine Learning 2019 - Lecture 15 - Clustering and Mixture models K-Means, DBSCAN, hierarchical clustering
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...Machine Learning with R Tutorial: Identifying Clustering Problems Make sure to like & comment if you liked this video! Take Hank's course here: ...Machine Learning with R Tutorial: Introduction to k-means Clustering Make sure to like & comment if you liked this video! This is the second video for our course Unsupervised Learning
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