Applied Unsupervised Learning with R

Applied Unsupervised Learning with R
Applied Unsupervised Learning with R
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 21m | 6.22 GB

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.

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 course 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 course 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 course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.


  • 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
Table of Contents

Introduction to Clustering Methods
1 Course Overview
2 Installation and Setup
3 Lesson Overview
4 Introduction to Clustering
5 Introduction to Iris Dataset
6 Introduction to k-means Clustering
7 Introduction to k-means Clustering with in-built Functions
8 Introduction to Market Segmentation
9 Introduction to k-medoids Clustering
10 Lesson Summary

Advanced Clustering Methods
11 Lesson Overview
12 Introduction to k-modes Clustering
13 Introduction to Density Based Clustering (DBSCAN)
14 Introduction to Hierarchical Clustering
15 Lesson Summary

Probability Distributions
16 Lesson Overview
17 Basic Terminology of Probability Distributions
18 Introduction to Kernel Density Estimation
19 Introduction to Kolmogorov-Smirnov Test
20 Lesson Summary

Dimension Reduction
21 Lesson Overview
22 Introduction
23 Market Basket Analysis
24 Passing through the Data to Find the Most Common Baskets
25 Principal Component Analysis
26 Lesson Summary

Data Comparison Methods
27 Lesson Overview
28 Introduction
29 Analytic Signatures
30 Comparison of Signatures
31 Applying Other Unsupervised Learning Methods to Analytic Signatures
32 Latent Variable Models – Factor Analysis
33 Lesson Summary

Anomaly Detection
34 Lesson Overview
35 Univariate Outlier Detection
36 Detecting Anomalies in Clusters
37 Contextual and Collective Anomaly
38 Kernel Density
39 Lesson Summary