Free Deep Learning & Machine Learning Courses

In this post, I want to share the Deep Learning and Machine Learning online courses open to everyone. If you know other courses, please share them with me so that I can append them to this list. If any of the listed courses are not available let me know to remove them. Thank you!

Last updated on 16 July 2021.

(With no specific order)

Google Machine Learning Crash Course

A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Yann LeCun’s Deep Learning Course at CDS

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Geoffrey E. Hinton’s Coursera Lectures

You can access the lecture slides and recordings of the Neural Networks for Machine Learning course. You can also visit Geoffrey E. Hinton’s web page at the University of Toronto for more resources.

Andrew NG’s Deep Learning Specialization on Coursera

The Deep Learning Specialization is the foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. Can I take the course for free? When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

CS50’s Introduction to Artificial Intelligence with Python

Even if you are not a student at Harvard, you are welcome to “take” this course for free via this OpenCourseWare by working your way through the course’s seven weeks of material.

Coursera Deep Learning in Computer Vision

The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing, and new image generation.

Global AI Hub Introduction to Machine Learning Training

Introduction to Machine Learning Training will take place for 10 hours in total with 2-hour programs for 5 days! We created the content of the education by using the sources of the world’s leading universities Stanford, Caltech, MIT, and Harvard! We will explore supervised and unsupervised learning in our 10-hour journey.

Murat Karakaya Machine Learning Lecture Series

CMPE468 Machine Learning for Engineers 2020 Spring & 2021 Spring. You can find a good start for Machine Learning concepts and methods without heavy math. The examples are solved by the Orange3 tool. So you do NOT need to know any Programming Language such as Python or so.

Stanford University Courses on Youtube

Stanford University shares recordings of many courses including CS229: Machine Learning, CS230: Deep Learning, etc. given by Andrew Ng.

Stanford University CS 329S: Machine Learning Systems Design

This course aims to provide an iterative framework for designing real-world machine learning systems. The goal of this framework is to build a system that is deployable, reliable, and scalable.

MIT OpenCourseWare on YouTube

MIT University shares recordings of many courses including MIT 6.034 Artificial Intelligence, MIT 6.S897 Machine Learning for Healthcare, etc.

Machine Learning Fundamentals by The University of California, San Diego on EDX

In this course, part of the Data Science Micro Masters program, you will learn a variety of supervised and unsupervised learning algorithms and the theory behind those algorithms. Understand machine learning’s role in data-driven modeling, prediction, and decision-making.

Machine Learning by the Carnegie Mellon University (Tom Mitchell and Maria-Florina Balcan)

We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam’s Razor.

Data Science: Machine Learning by the Harvard University on EDX

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?

Data Science: Probability by the Harvard University on EDX

What you’ll learn:

Important concepts in probability theory including random variables and independence
How to perform a Monte Carlo simulation
The meaning of expected values and standard errors and how to compute them in R
The importance of the Central Limit Theorem