Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur[3]

DeepMind. Reinforcement Learning Course by David Silver[4]

Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition.[5]

[0] http://www.fast.ai/

[1] https://eu.udacity.com/course/intro-to-machine-learning--ud1...

[2] https://agi.mit.edu/

[3] https://www.youtube.com/watch?v=eLbMPyrw4rw&list=PL6EE0CD029...

[4] https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r...

[5] https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rB...

[+] kmax12|8 years ago|reply

One important skill you will need is feature engineering. Formal methods for it aren’t typically in ML ciriculums, but it’s worth understanding if you’re interested in applications if ML.

Deep learning addresses it to some extent, but isn’t always the best choice if you don’t have image / text data (eg tabular datasets from databases, log files) or a lot of training examples.

I’m the developer of a library called Featuretools (https://github.com/Featuretools/featuretools) which is a good tool to know for automated feature engineering. Our demos are also a useful resource to learn using some interesting datasets and problems: https://www.featuretools.com/demos

[+] cuchoi|8 years ago|reply

I am playing with an encrypted dataset, so this comes very useful. Any tips on how to take advantage of it or about its strengths and weaknesses?

[+] jacek|8 years ago|reply

Not a video course, but an excellent book: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron. Provides a gentle and high quality introduction to Machine Learning with practice from the very beginning. I wish I had this book when I was starting. It explains everything from data engineering, through how algorithms work, to practical applications. Everything in Python 3, covering pandas, scikit-learn, tensorflow. It is absolutely wonderful!

[1] http://shop.oreilly.com/product/0636920052289.do

[+] colmvp|8 years ago|reply

As a person who did a lot of Deep Learning... learning in 2017, I think this was my favorite.

Fast.ai was fine, but I felt like most of my learning for the things I cared about came from reading research papers, watching Karpathy's CS231n lectures, and blog posts that went into detail on particular concepts.

But when at certain points I felt confused on certain concepts, Geron's book did a pretty good job explaining things slowly and in great detail, especially with respects to the code he wrote. It's still a book I'll pick up for 20-40 minutes every other day to help my mind recall about how something works.

Funnily enough, I've spent the last few months reading Sutton/Barto's Intro to Reinforcement Learning (along with Silver's lectures on DeepMind's YouTube Channel) and only realized Geron touches upon RL a little bit in the latter part of the ML book.

[+] visarga|8 years ago|reply

Have you tried Andrew Ng's course? And I don't mean the one from the start of Coursera, but the original Stanford ML Course he taught at the blackboard. This ML course has all the math done by hand instead of skipped over.

https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

[+] WhitneyLand|8 years ago|reply

bad link?

[+] DLTarasi|8 years ago|reply

I'll give another recommendation for http://www.fast.ai/

I went through the first phase of the course as an intro to AI/DL and thought it was really great from a high-level perspective. If you have a decent understanding of Python you'll have a working model running on AWS within the first few hours of the course which is very rewarding.

It does a better job than I expected explaining the underlying intuition of the math, but doesn't dive deep into the actual formulas. There are obviously tradeoffs to this approach and if you want to continue in the field you'll need to do something to fill in this background, but as far getting your hands dirty and understanding the basics I really liked the fast.ai approach.

[+] hackernewsacct|8 years ago|reply

As a beginner I cannot recommend this class in its current form. The first lecture with its setup walk through is outdated and I have trouble understanding how to do a work around.

[+] FabHK|8 years ago|reply

I liked Yaser Abu-Mostafa's Caltech ML course. A bit dated (2012), but solid introduction to the basics (such as VC dimensions). However, given your stated preferences, other recommendations (such as fast.ai) are probably better suited.

https://work.caltech.edu/telecourse.html

[+] Eridrus|8 years ago|reply

I found this course pretty interesting, since it gives some perspective on what sorts of guarantees people want that DL doesn't provide, but I wouldn't necessarily recommend starting with it unless you have a theory bent.

[+] aaronsnoswell|8 years ago|reply

If you're interested in learning Reinforcement Learning, then I can't recommend David Silver's lecture series highly enough. Youtube videos and slides are available for the entire thing. http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html

[+] deepGem|8 years ago|reply

I wouldn't really classify this as an introductory course. Some background in A* search, Markov processes etc is required. David has a fantastic narration style and this is one of those courses where I had to step out because I had other responsibilities and not because I got bored with the content.

[+] FullMtlAlcoholc|8 years ago|reply

https://cs231n.github.io/

[+] joshuaeckroth|8 years ago|reply

If you want to see projects coded in Python (random forests, word2vec, convolutional neural nets), I recently finished producing this set of videos: https://www.packtpub.com/big-data-and-business-intelligence/...

[+] jack_pp|8 years ago|reply

I've learned a ton from https://www.kadenze.com/courses/creative-applications-of-dee...

[+] alexnewman|8 years ago|reply

As much as I love Ng, I vastly prefer Hinton’s lectures. Something about that English accent.

[+] TheAlchemist|8 years ago|reply

If you speak french, I would recommend Hugo Larochelle channel:

https://www.youtube.com/user/hugolarochelle

[+] oscgonfer|8 years ago|reply

I took the coursera specialisation one week ago. It takes you from the very basics to some more complex modules like keras or tensorflow. If you are into it and have time, the whole 4 courses can be done in the free week: https://www.coursera.org/specializations/deep-learning

[+] nshr|8 years ago|reply

Fast.ai, you can checkout the forum here: http://forums.fast.ai/

[+] zengid|8 years ago|reply

I'll add another vote for course.fast.ai since I'm currently working through it. The instructors are serious about delivering a practical course that will get you right into the process of training and playing around with the code. Also, one of the best parts of their courses (they're offering a few simultaneously) is the community of learners that hang around on the forums.

[+] ivansavz|8 years ago|reply

I found the "Bay Area Deep Learning School" 2-day lecture series to be very good:

https://www.youtube.com/watch?v=eyovmAtoUx0

https://www.youtube.com/watch?v=9dXiAecyJrY

[+] Lausbert|8 years ago|reply

A pretty short introduction to machine learning created by myself: http://lausbert.com/2018/01/14/the-shortest-introduction-to-...

[+] aficionado|8 years ago|reply

Ideal to practice, learn, and teach machine learning:

https://bigml.com/ml101 https://bigml.com/education/videos

[+] max_|8 years ago|reply

MIT 6.034 Artificial Intelligence, Fall 2010

https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP...

[+] f00_|8 years ago|reply

getting your feet wet

andrew ng's machine learning course: https://www.coursera.org/learn/machine-learning

to get up to date on convnet architecture

Fei-Fei Li and Karpathy's cs231n: https://cs231n.github.io/

if you want to go deep

geoff hinton's neural networks for machine learning coursera: https://www.coursera.org/learn/neural-networks