Free Course

Machine Learning: Unsupervised Learning

by
Georgia Institute of Technology

Conversations on Analyzing Data

Nanodegree Program

Machine Learning Engineer

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About this Course

This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.

Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

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Course Cost
Free
Timeline
Approx. 1 months
Skill Level
intermediate
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Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

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This course is your first step towards a new career with the Machine Learning Engineer Program.

Free Course

Machine Learning: Unsupervised Learning

byGeorgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

Charles Isbell

Charles Isbell

Instructor

Michael Littman

Michael Littman

Instructor

Pushkar Kolhe

Pushkar Kolhe

Instructor

Prerequisites and Requirements

This class will assume that you have programming experience as you will be expected to work with python libraries such as numpy and scikit. A good grasp of probability and statistics is also required. Udacity's Intro to Statistics, especially Lessons 8, 9 and 10, may be a useful refresher.

An introductory course like Udacity's Introduction to Artificial Intelligence also provides a helpful background for this course.

See the Technology Requirements for using Udacity.

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Free Courses

Rich Learning Content

Interactive Quizzes

Self Paced Learning

Taught by Industry Professionals

 

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Why Take This Course

You will learn about and practice a variety of Unsupervised Learning approaches, including: randomized optimization, clustering, feature selection and transformation, and information theory.

You will learn important Machine Learning methods, techniques and best practices, and will gain experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!).

What do I get?
Instructor videosLearn by doing exercisesTaught by industry professionals

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