Self Paced Course - Start anytime

Machine Learning 2 - Unsupervised Learning (Udacity)

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.

You can expect to spend 8-10 hours per week on this course.

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.

- Machine Learning 1: Supervised Learning

- Machine Learning 2: Unsupervised Learning (this course)

- Machine Learning 3: Reinforcement Learning

If you are new to Machine Learning, we recommend you take these 3 courses in order.

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

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.

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