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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic Python experience looking to design algorithms

Who should attend & recommended skills

  • This course is geared for Python developers, analysts or others with Python skills who wish to design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.
  • Skill-level: Foundation-level Applied Unsupervised Learning with R skills for Intermediate skilled team members.
  • This is not a basic class.
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su.

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Applied Unsupervised Learning with R expert instructor, participants will learn about and explore:
  • Building state-of-the-art algorithms that can solve your business’ problems
  • Learning how to find hidden patterns in your data
  • Revising key concepts with hands-on exercises using real-world datasets.
  • Implementing clustering methods such as k-means, agglomerative, and divisive
  • Writing code in R to analyze market segmentation and consumer behavior
  • Estimating distribution and probabilities of different outcomes
  • Implementing dimension reduction using principal component analysis
  • Appling anomaly detection methods to identify fraud
  • Designing algorithms with R and learn how to edit or improve code

Course breakdown / modules

  • 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

  • Introduction
  • Introduction to k-modes Clustering
  • Introduction to Density-Based Clustering (DBSCAN)

  • Introduction
  • Basic Terminology of Probability Distributions
  • Introduction to Kernel Density Estimation
  • Introduction to the Kolmogorov-Smirnov Test

  • Introduction
  • Market Basket Analysis

  • Introduction
  • Analytic Signatures
  • Comparison of Signatures
  • Latent Variable Models – Factor Analysis

  • Introduction
  • Univariate Outlier Detection
  • Kernel Density