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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. 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.

LEARNING OUTCOMES

  • 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

Applied Unsupervised Learning with R

Course Code

GTDULR

Duration

2 Days

Course Fee

POA

Accreditation

N/A

Target 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 course is for beginners, it will be beneficial to have some basic  familiarity with R. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians. 

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Course Description

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. 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.

LEARNING OUTCOMES

  • 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
Course Outline

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
Learning Path
Ways to Attend
  • Attend a public course, if there is one available. Please check our schedule, or register your interest in joining a course in your area.
  • Private onsite Team training also available, please contact us to discuss. We can customise this course to suit your business requirements.

Private Team Training is available for this course

We deliver this course either on or off-site in various regions around the world, and can customise your delivery to suit your exact business needs. Talk to us about how we can fine-tune a course to suit your team's current skillset and ultimate learning objectives.

Private Team Training | Contact us

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Private Team Training

Our instructors are specialist consultants with vast real world experience and expertise allowing them to design and deliver client-focused courses for your organisation.

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