**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.

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

**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

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