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Instructor
Dr. Yogesh Kulkarni, Instructor - Unsupervised Learning: Dimensionality Reduction and Representation

Dr. Yogesh Kulkarni

16+ years in CAD/Engineering software development, in various capacities, including R & D group and site manager. Recently finished a PhD in Geometric Modeling. Currently working as a Data Analytics consultant, in areas such as Natural Language Processing, Text Mining, Machine Learning and Deep Learning.
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Instructor: Dr. Yogesh Kulkarni

Overcoming the Curse of Dimensionality

  • Understand dimension reduction techniques, problems associated with it, and its practical applications.  
  • Instructor holds a PhD in Geometric Modeling and works in areas such as NLP and Deep Learning.
  • Continue towards your Machine Learning certification with Experfy

Duration: 2h 20m

Course Description

In the era of data deluge its important to separate relevant from irrelevant, i.e. like segregating chaff from the grains. Dimension Reduction techniques are widely used to identify relevant features (or combinations) representing the underlying structure of the data.

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What am I going to get from this course?

• Understand dimension reduction techniques, problems associated with it, and its practical applications.
• Understand techniques to reduce dimensions without harming important variables
• Learn application of Dimension Reduction techniques for practical problems

Prerequisites and Target Audience

What will students need to know or do before starting this course?

• Familiarity with Linear Algebra, Probability, Statistics
• Familiarity with Computer Programming, preferably Python.

Who should take this course? Who should not?

Students or professionals with formal college education in Science and Mathematics

Curriculum

Module 1: Background

53:03
Lecture 1 Introduction
46:46
Lecture 2 Data Pre-Processing
06:17

Module 2: Dimensionality Reduction and Representation

01:03:58
Lecture 3 Principal Component Analysis I
14:56
Lecture 4 Principal Component Analysis II
12:00
Lecture 5 Principal Component Analysis III
04:22
Lecture 6 Sparse Coding
09:18
Lecture 7 Independent Component Analysis
14:50
Lecture 8 Self Organizing Maps
08:32

Module 3: Hands-On

20:57
Lecture 9 Mini Project I
04:44
Lecture 10 Mini-Project II
05:51
Lecture 11 Assignment (Homework)
09:58
Quiz 1 Quiz
Lecture 12 References
00:24