Data Science-Unsupervised Machine Learning Using R
4.3 (17 ratings)
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Data Science-Unsupervised Machine Learning Using R

Recommender Systems, Association Rules, Dimension Reduction, Network Analysis
4.3 (17 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
735 students enrolled
Created by ExcelR Solutions
Last updated 1/2017
Current price: $12 Original price: $50 Discount: 76% off
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What Will I Learn?
  • Will have an understanding on topics related to data mining it applications and methods of performing the same. You will also be introduced to real life applications of these methods ..
View Curriculum
  • Download R & RStudio before starting this tutorial
  • Download datasets folder in zipfile which is uploaded in session 1
  • While no prerequisite is required for taking up this course, however rudimentary knowledge of Basic statistics will be an added advantage

Data Science-Unsupervised Machine Learning Using R is designed to cover majority of the capabilities of R from Analytics & Data Science perspective,

  • To be a successful data scientist one must be armed with a plethora of tools to tackle the trickiest of problems. 
  • Data mining, forms biggest chunk of topics that one needs to be trained on to be a good data scientist. 
  • The unsupervised learning which is a part of Data mining is one of the most important topics.  

As a part of this course you will be introduced to a series of topics under this umbrella and will be provided with real life use cases for the same 

Who is the target audience?
  • All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.
  • This course is a part of series of courses on Data Science and care has been taken to ensure that each module is complete in itself; participants seeking to make a career in data analytics
Compare to Other R Courses
Curriculum For This Course
27 Lectures
Data Mining-Unsupervised Learning Using R
1 Lecture 05:23
Recommender Systems
4 Lectures 35:55

Learn about Collaborative Filtering; measure of the similarity of the users; various measures of similarity amongst users

Preview 11:56

Learn about Various criterion for recommending;

What Item to Recommend..?

Disadvantages of Recommender systems; circumventing the problem by clustering. PCA; discarding the unpopular items; 

Recommender System Disadvantage

Learn Computation reduction techniques; 

Recommendation Reduction Process

4 questions
Association Rules
9 Lectures 01:33:19

Learn about Market Basket Analysis, Relationship mining, Affinity analysis; The analogy of supermarket; How is it different from online recommender systems

Association Rules Introduction

Learn The population of data through POS and also The definition of a transaction

Market Basket Analysis

Learn What goes with what; do any pairs of groups exist among the products; how can this information be used

Association Rules Part -1

Learn about Product bundling; Stocking; Racking; Association Rules in other than retail stores

Association Rules Part 2

Learn about Converting the list to format data to binary data; listing possible rules’; Antecedent and Consequent;

Case Study and Terminology

Learn about The Performance measure - Support, Confidence, Lift; The formula for Support; support criterion is based on frequency; The Apriori Algorithm

Performance Measures and Support Calculation

Learn about The Performance measure - Support, Confidence, Lift; The formula for Support; support criterion is based on frequency; The Apriori Algorithm

Confidance Calculation

‘Lift Ratio’ -  a variant of the Confidence measure; Lift ratio is a ratio the dependencies and independencies of the antecedent and the consequent

Lift Calculation

Learn The flow path for formulating association rules; Drawback of Association rules - May produce absurd and interesting rules, Profusion of rules; Other applications of Association rules

Rules Selection Process and Applications

9 questions
Dimension Reduction
10 Lectures 01:45:28

Learn Why dimension reduction; the types of dimension reduction

Dimension Reduction Introduction

Learn about Computational speeds; Face Recognition (Facebook’s Deepface); Image Compression

Dimension Reduction Applications

Learn about Reduction in number of columns; Analyse relations between columns; Visualisation of Multidimensional data in 2D; From ‘All information’ to ‘most of the information’

PCA Key Benefits

Learn About  analogy of multiple school quizzes and capturing most of information from many in one

PCA Intuition

Learn No of PCs is equal to No of columns; Difference between PCs and the original Columns; The rationale behind the selection of the Weight for computation of the PC - maximization of variance principle;

PCA Preliminaries and Weights

Learn Why to Standardize; The math for obtaining the Principal Component from the Principal Component Weight;

Standardize Variables and PCA Calculation

Learn about Data Compression; How much information is enough - Consult with Domain experts; understanding data compression with matrices;

PCA First Goal

Learn about Labelling of Principal components  - detailed study of the Principal component weights;

PCA Second Goal

Learn Visualization in 2D; possibility of visualizing the clustering; Batch processing; Analysis of multivariate data; Visualization to spot outliers; Brain Gym

PCA Third Goal and Additional Benifits

Learn Recap of PCA - ordered by variance, Recap of SVD, Recap of association rules - Support, Confidence, Lift Ratio;

Recap Dimension Reduction and Association Rules

9 questions
Network Analysis
3 Lectures 29:43

Preliminaries about networks or graphs, Adjacency matrix as a node and edge notation, difference between, unidirectional, bidirectional or undirected graph; Introduction to social media network.

Network Analysis Introduction

Bank to borrower network based on number of links and context, peer to peer lending and its growth over the years.

Peer To Peer Network Analysis

Nodes and edges for analysis for strength; understanding of Business context/problem; Nodes! what can they be; Network of actors and the movie revenues.

Social Media Network Analysis
About the Instructor
ExcelR Solutions
4.0 Average rating
756 Reviews
7,699 Students
8 Courses
Pioneer in professional management trainings & consulting


Certified Six Sigma Master Black Belt

Project Management Professional (PMP)

Agile Certified Practitioner (PMI - ACP)

Risk Management Professional (PMI-RMP)

Certified Scrum Master

Agile Project Management – Foundation & Practitioner from APMG

Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15+ years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc.

He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes.

He has 8 years of extensive experience in corporate, open house and online training.

He is a thorough implementer with abilities in Business Analytics and Agile projects.

He worked in Delivery management focusing on maximizing business value articulation.

He has a comprehensive experience in leading teams and multiple projects.

Quality Management: A thorough implementer with abilities in Quality management focusing on maximizing customer satisfaction, process compliance and business value articulation; comprehensive experience in leading teams & multiple projects. A result-oriented leader with expertise in devising strategies aimed at enhancing overall organizational growth, sustained profitability of operations and improved business performance.

Project Management: Project Management Professional involved in Initiation, Planning, Execution, Monitoring & Controlling and Closing phases of project activities. Devising and implementing project plans within preset budgets and deadlines and managing the projects towards successful delivery of project deliverables and meeting project objectives.

Training: Close to 8 years training experience and conducted multiple trainings in PMP, Agile, Six Sigma, Business Analytics and Process Excellence across the globe. Understands the individual differences of the attendees and possesses excellent training skills and considered as one of the best trainers in his areas of expertise.