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In case you missed my previous tweet, these are some of the machine learning courses of 2020 worth checking out.
I added a deep reinforcement learning one and production ML course to the list. IMO, this would make a great semester of learning ML for anyone.
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I added a deep reinforcement learning one and production ML course to the list. IMO, this would make a great semester of learning ML for anyone.
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Introduction to Machine LearningI think it was time that we have a new introduction to machine learning course. I like Sebastian's course as it covers topics that are essential in ML but I just overall enjoy the teaching style.
https://sebastianraschka.com/resources/ml-lectures-1.html
Deep Learning (with PyTorch)If you are looking for an updated course on deep learning, this one might interest you. The first lecture is my favorite but all the lectures include important topics in deep learning.
by Alfredo Canziani and Yann LeCun
https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
CMU Multilingual NLP 2020To keep myself informed about important topics in NLP, I have been going through some of the lectures in this course by Graham Neubig. Graham's courses have been very useful for me to stay on top of things in NLP.
https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5
Deep Learning for Computer VisionI appreciate the comprehensiveness of this course. I think it's a great course to get introduced to different topics in computer vision. There are lectures on the latest developments as well.
by Justin Johnson
https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r
UC Berkeley CS 285: Deep Reinforcement Learning. Emphasis on transfer learning, meta-learning, and open problems.
by Sergey Levine
https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
Full Stack Deep LearningLearn about all the important steps in taking ML systems into production. From setting up your project all the way to monitoring ML systems. An excellent course. I enjoyed it.
by @full_stack_dl
Final Words! Don't burnt out yourself. Take your time learning from these courses. It's a lot of content to consume. Make sure to practice code along the way. Read papers, write notes down, ask around if you are confused about something. Every little progress counts!
