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Applied Unsupervised Learning with R

  • Course Code: Artificial Intelligence - Applied Unsupervised Learning with R
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python developers, analysts or others who wants to Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Applied Unsupervised Learning with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python developers, analysts or others who wants to Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Applied Unsupervised Learning with R expert instructor, students will learn about and explore: 

  • Build state-of-the-art algorithms that can solve your business’ problems 
  • Learn how to find hidden patterns in your data 
  • Revise key concepts with hands-on exercises using real-world datasets. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. 

Pre-Requisites:  Students should have   

  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Clustering Methods 
  • 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 
  1. Advanced Clustering Methods 
  • Advanced Clustering Methods 
  • Introduction 
  • Introduction to k-modes Clustering 
  • Introduction to Density-Based Clustering (DBSCAN) 
  1. Probability Distributions 
  • Probability Distributions 
  • Introduction 
  • Basic Terminology of Probability Distributions 
  • Introduction to Kernel Density Estimation 
  • Introduction to the Kolmogorov-Smirnov Test 
  1. Dimension Reduction 
  • Dimension Reduction 
  • Introduction 
  • Market Basket Analysis 
  1. Data Comparison Methods 
  • Data Comparison Methods 
  • Introduction 
  • Analytic Signatures 
  • Comparison of Signatures 
  • Latent Variable Models – Factor Analysis 
  1. Anomaly Detection 
  • Anomaly Detection 
  • Introduction 
  • Univariate Outlier Detection 
  • Kernel Density 
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