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Instructor

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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.
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
08:24
Lecture 8 How to decide K?
08:48
Lecture 9 Example through R
23:27
Lecture 10 Further Discussions
03:58
Module 3: Hierarchical Clustering
Lecture 11 Introduction
11:04

Understand extended distance measures, dendrogram etc.

Lecture 12 Quick Algorithm
07:53
Lecture 13 Cluster number determination
08:01

Lecture 14 Example through R
18:35
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

8 Reviews

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David K

December, 2016

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William C

May, 2017

This course is an all-encompassing and enthusiastic learning experience of most popular set of Cluster algorithms and analysis. It was educative and collaborative with end-to-end examples and hands-on practice exercises. It helped me learn quickly the data mining techniques in my functional needs in marketing and campaign efforts. This course specifically taught the various scenarios where clustering is necessary and showed very well clustering techniques appropriate for a scenario. The examples provided in the course were excellent and very useful. I find the lecturer friendly and professional. It was a nice participation experience for me.

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Norbert K

May, 2017

This course was very informative and teaches many perspectives of clustering. As a beginner interested in changing career in data science, this course helped me enrich my knowledge in both ways theoretically and practically.

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Colin S

May, 2017

It is a fascinating course. Though I was an experience domain expert, I still took this course to gain practical understanding on clustering to derive business insights in my present role. Whether you are a beginner of experienced it is a great course to enrich your knowledge in analytics and data science.

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Jeff J

July, 2017

A remarkable class that provides you a perfect introduction to clustering and gets you through to fitting an intermediary standard user. I very much recommend it if this is the area you're looking to get into or the experience you wish to include on your resume.

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Sheetal C

July, 2017

Very good analysis. The lectures are straightforward and easy to understand. The subject matter is very suitable to practical application use.

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Robert T

July, 2017

A fine preparatory study of clustering. Very resourceful. I just figure that there could be better help with reference to the practice tests. Otherwise the lectures by the instructor were really amazing

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Jerry C

July, 2017

The instructor has masterfully communicated the elements and precisely crafted the curriculum components, therefore the course is both simple to keep up and you gain a tremendous amout of knowledge.

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