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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.

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

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.

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


Module 1: Background

Lecture 1 Introduction
Lecture 2 Data Pre-Processing

Module 2: Dimensionality Reduction and Representation

Lecture 3 Principal Component Analysis I
Lecture 4 Principal Component Analysis II
Lecture 5 Principal Component Analysis III
Lecture 6 Sparse Coding
Lecture 7 Independent Component Analysis
Lecture 8 Self Organizing Maps

Module 3: Hands-On

Lecture 9 Mini Project I
Lecture 10 Mini-Project II
Lecture 11 Assignment (Homework)
Quiz 1 Quiz
Lecture 12 References