Applied Unsupervised Learning with R
Course Description Overview
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.
After completing this course, you will be able to:
- 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
- OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
- R (3.0.0 or more recent, available for free at https://cran.r-project.org/)
For the optimal student experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM
- Storage: 5 GB available space
- An internet connection
Lesson 1: Introduction to Clustering Methods
- Introduction
- Introduction to Clustering
- Introduction to the Iris Dataset
- Introduction to k-means Clustering
- Introduction to k-means Clustering with Built-In Functions
- Introduction to Market Segmentation
- Introduction to k-medoids Clustering
Lesson 2: Advanced Clustering Methods
- Introduction
- Introduction to k-modes Clustering
- Introduction to Density-Based Clustering (DBSCAN)
Lesson 3: Probability Distributions
- Introduction
- Basic Terminology of Probability Distributions
- Introduction to Kernel Density Estimation
- Introduction to the Kolmogorov-Smirnov Test
Lesson 4: Dimension Reduction
- Introduction
- Market Basket Analysis
Lesson 5: Data Comparison Methods
- Introduction
- Analytic Signatures
- Latent Variable Models – Factor Analysis
Lesson 6: Anomaly Detection
- Introduction
- Univariate Outlier Detection
- Kernel Density