10 students

Artificial Intelligence & Machine Learning

Part 2 – Unsupervised Learning

Asynchronous Online Course

 

  • Length: 6 weeks
  • Instructor: Dr. Azam Rabiee

Important Notes: 

  • A “Certificate of Completion” will be awarded to you, if you complete at least 60% of the course.

Course Description

If you are here, it means that you already know that machine learning projects are divided into three main categories of supervised, unsupervised, and semi-supervised. In this course, we are going to see how unsupervised learning works.

You will learn the basics of clustering and the well-known k-means algorithm. Then, we will review the dimension reduction techniques, and will focus on Principle Component Analysis (PCA) as a traditional algorithm for dimension reduction.

The attractive part of the course is when we review the two most recent popular deep learning architectures: Autoencoders and Generative Adversarial Networks (GANs).
Autoencoders are beneficial for dimension reduction, data representation, feature extraction, as well as image (or speech) compression and denoising.

The impressive GANs are the state-of-the-art generative models applicable in image and audio generation, as well as image-to-image translation, text-to-image translation and so on.

Avoiding from the complex mathematics, this course comprehensibly presents the underlying concepts of the deep learning models. The concepts are supported by Lab session(s).

 

Prerequisites

 

Resources

 

The AI & ML Series

Unsupervised Learning is the second course in the AI & ML series. List of courses in the AI & ML boot camp is as follows:

  1. Introduction to AI & ML
  2. Unsupervised Learning
  3. Sequence models
  4. Reinforcement Learning
  5. Convolutional Neural Networks

Instructor

Daryoush Mortazavi, PhD, PEng, is the founder and CEO of 'Synnovate Institute of Research & Education (SIRE)'

$30.00

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