$129

Need Custom Training for Your Team?

Get Quote

Call Us

Toll Free (844) 397-3739

Inquire About This Course

Instructor

Thumb 4cc39eba 542c 4ff9 9f83 ca1cac088939

Anirban Ghosh

Anirban Ghosh is a Machine Learning Scientist and has been part of organizations e.g. Genpact, Target and Wipro. His experience spans over various sectors e.g. Retail, Telecom, Travel/Hospitality etc. He has more than 9 years of experience in Analytics and Data Sciences. He holds bachelor's in Statistics from St.Xavier's College, Calcutta and master's in Applied Statistics & Computing from IIT, Bombay.

Clustering and Association Rule Mining

Instructor: Anirban Ghosh

Learn Clustering methods and Association Rule Mining Techniques

  • Learn concepts of Cluster Analysis and study most popular set of Clustering algorithms with end-to-end examples in R
  • Supported by office hours and hands-on practice exercises to be submitted at the end of the course 
  • Instructor:Machine Learning Scientist with 9+ years of hands-on experience in predictive analytics domain at companies like Target, Symphony-IRI and Genpact 

Course Description

Clustering and Association Rule Mining are two of the most frequently used Data Mining technique for various functional needs, especially in Marketing, Merchandising, and Campaign efforts. Clustering helps find natural and inherent structures amongst the objects, where as Association Rule is a very powerful way to identify interesting relations between objects in large commercial databases. The main motivation for the course is: i) This course specifically touches upon the scenarios where Clustering is necessary, and which Clustering technique is appropriate for which scenario. ii) This course also stresses on advantages as well as practical issues with different Clustering techniques

What am I going to get from this course?
  • Learn clustering through examples in R – that you immediately apply in your day-to-day work
  • Over 20 lectures and 5-6 hours of content, plus 2 practice exercises on Clustering and Market Basket Analysis
  • Learn practical Hierarchical, Non-Hierarchical, Density based clustering techniques. Also Association rules and Market Basket Analysis

Prerequisites and Target Audience

What will students need to know or do before starting this course?
  • Fundamental understanding of Statistics/Mathematics, specially Probability, Set Theory, Distance Measure, Matrix Algebra etc.
  • All examples in this course will be run in R. So, prior understanding of R is desirable.
Who should take this course? Who should not?
  • Beginners who want to change careers to Data Science and wish to enrich their horizon of knowledge with both theory and examples can also take this course
  • Experienced Domain Experts who intent to gain solid practical understanding on Clustering and use that to derive business insights in their current role
  • Analytics and Data Science professionals who want to refresh their skills in Clustering

Curriculum

Module 1: Overview of Unsupervised Learning
Lecture 1 Introduction to Unsupervised Learning
11:49

Review the basics of Unsupervised Learning

Lecture 2 Exploratory Data Analysis
11:24

Understand Exploratory Data Analysis (EDA) with R sessions

Lecture 3 R Session on Exploratory Data Analysis (Part 1)
12:21

Practical R-session on Correlation

Lecture 4 R Session on Exploratory Data Analysis (Part 2)
11:54

Practical R-session on Principal Components

Lecture 5 Introduction to Clustering
05:55

Get started to understand the Clustering basics

Module 2: K-Means Clustering
Lecture 6 Introductory Basics
17:33

Understand different mathematical basics and terminologies needed for K-Means

Lecture 7 K-Means Algorithm
Lecture 8 How to decide K?
Lecture 9 Example through R
23:27
Lecture 10 Further Discussions
Module 3: Hierarchical Clustering
Lecture 11 Introduction
11:04

Understand extended distance measures, dendrogram etc.

Lecture 12 Quick Algorithm
Lecture 13 Cluster number determination

Lecture 14 Example through R
Lecture 15 Further Discussions
04:58
Module 4: Density based Clustering
Lecture 16 Introduction & Terminologies
16:13
Lecture 17 DBSCAN Algorithm
06:05
Lecture 18 Choice of parameters
13:24

How do we empirically choose optimal parameters?

Lecture 19 Example through R
15:29
Lecture 20 Further Discussions
03:45
Module 5: Association Rules (AR)
Lecture 21 Introduction
13:05
Lecture 22 The Apriori Nature
15:34
Lecture 23 Market Basket Analysis
10:16
Lecture 24 Example through R
20:00
Lecture 25 Further Discussions
03:41
Lecture 26 Practice Exercises on Clustering & Association Rule

2 practice exercises, each on Clustering and Association Rule Mining

Reviews

1 Review

Empty user
David K

December, 2016

350