
Ask HN: Best introductory video courses on ML and Deep Learning? - rayalez
Hi there! I&#x27;m looking for an easy way to get started with AI&#x2F;ML&#x2F;DL.<p>I don&#x27;t necessarily need to go super deep into details, I&#x27;m more interested in a practical high-level overview.<p>I know about Andrew Ng course [1], 3blue1brown videos [2], and Berkeley AI course [3]. What else would you recommend?<p>[1] https:&#x2F;&#x2F;www.coursera.org&#x2F;learn&#x2F;machine-learning<p>[2] https:&#x2F;&#x2F;www.youtube.com&#x2F;playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi<p>[3] https:&#x2F;&#x2F;www.youtube.com&#x2F;playlist?list=PLIeooNSdhQE5kRrB71yu5yP9BRCJCSbMt
======
mindcrime
[http://www.fast.ai/](http://www.fast.ai/)

[https://www.udacity.com/course/intro-to-machine-learning--
ud...](https://www.udacity.com/course/intro-to-machine-learning--ud120)

[https://agi.mit.edu/](https://agi.mit.edu/)

[https://www.youtube.com/watch?v=eLbMPyrw4rw&list=PL6EE0CD029...](https://www.youtube.com/watch?v=eLbMPyrw4rw&list=PL6EE0CD02910E57B8)

[https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r...](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT)

[https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rB...](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)

~~~
igravious
fast.ai Making neural nets uncool again. fast.ai is dedicated to making the
power of deep learning accessible to all.[0]

Udacity. Intro to Machine Learning: Pattern Recognition for Fun and Profit[1]

MIT 6.S099: Artificial General Intelligence[2]

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/](http://www.fast.ai/)

[1] [https://eu.udacity.com/course/intro-to-machine-learning--
ud1...](https://eu.udacity.com/course/intro-to-machine-learning--ud120)

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

[3]
[https://www.youtube.com/watch?v=eLbMPyrw4rw&list=PL6EE0CD029...](https://www.youtube.com/watch?v=eLbMPyrw4rw&list=PL6EE0CD02910E57B8)

[4]
[https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r...](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT)

[5]
[https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rB...](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)

------
kmax12
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](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](https://www.featuretools.com/demos)

~~~
cuchoi
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
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](http://shop.oreilly.com/product/0636920052289.do)

~~~
colmvp
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
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](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599)

~~~
WhitneyLand
bad link?

~~~
evex
[https://www.youtube.com/playlist?list=A89DCFA6ADACE599](https://www.youtube.com/playlist?list=A89DCFA6ADACE599)

------
DLTarasi
I'll give another recommendation for
[http://www.fast.ai/](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
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.

~~~
tinymollusk
Part 1v2 was just released, it uses pytorch. Check it out, I was in the fall
fellowship and can heartily recommend it.

~~~
hackernewsacct
This is welcomed news, thank you for the update. Can you link to v2? On the
site I just see part 1, v1 with the AWS set up. This is the beginner
unfriendly/outdated one I am referring to, and I don't see the updated version
you're referring to.

~~~
tinymollusk
I just checked the course forum, and Jeremy asked us not to share on high
traffic sites until theyve finished the new website. I think it'll be out
within a week or so, so check the fast.ai site for updates. Should be soon!

~~~
hackernewsacct
Okay, I will check in a week or so. Thanks.

------
FabHK
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](https://work.caltech.edu/telecourse.html)

~~~
Eridrus
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
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](http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html)

~~~
deepGem
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
[https://cs231n.github.io/](https://cs231n.github.io/)

------
joshuaeckroth
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/...](https://www.packtpub.com/big-data-and-business-
intelligence/python-artificial-intelligence-projects-beginners-video)

------
jack_pp
I've learned a ton from [https://www.kadenze.com/courses/creative-
applications-of-dee...](https://www.kadenze.com/courses/creative-applications-
of-deep-learning-with-tensorflow/info)

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

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

[https://www.youtube.com/user/hugolarochelle](https://www.youtube.com/user/hugolarochelle)

------
oscgonfer
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](https://www.coursera.org/specializations/deep-learning)

------
nshr
Fast.ai, you can checkout the forum here:
[http://forums.fast.ai/](http://forums.fast.ai/)

------
zengid
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.

------
ivan_ah
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=eyovmAtoUx0)

[https://www.youtube.com/watch?v=9dXiAecyJrY](https://www.youtube.com/watch?v=9dXiAecyJrY)

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

------
aficionado
Ideal to practice, learn, and teach machine learning:

[https://bigml.com/ml101](https://bigml.com/ml101)
[https://bigml.com/education/videos](https://bigml.com/education/videos)

------
max_
MIT 6.034 Artificial Intelligence, Fall 2010

[https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP...](https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP63gFHB6xb-
kVBiQHYe_4hSi)

------
f00_
getting your feet wet

andrew ng's machine learning course: [https://www.coursera.org/learn/machine-
learning](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/](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](https://www.coursera.org/learn/neural-networks)

------
daniyalnawaz
Andrew Ng's deeplearning.ai course is pretty accessible for beginners.

------
dnl_pozzobon
How about Microsoft's AI School
[https://aischool.microsoft.com](https://aischool.microsoft.com)

------
trias
this talk really enlightened me: [https://www.infoq.com/presentations/neural-
networks-introduc...](https://www.infoq.com/presentations/neural-networks-
introduction)

it is not a full course, but more an introduction.

------
nl
Fast.ai

------
kanodiaayush
Can we name the links so that we know what each is? @mindcrime

~~~
mindcrime
I would have but I was half asleep when I first posted that. Luckily someone
else came along and labeled them in a reply. :-)

------
rwieruch
Just recently I have written a "Machine Learning for Web Developers in
JavaScript" blog post [0]. If you are a web developer, it might be interesting
for you. It outlines my approach of learning it and gives a couple of great
resources for JavaScript enthusiasts. Otherwise, I will just post a couple of
the materials I used below. It's not only video courses, because I believe
it's always useful to stimulate all senses.

\- [0] [https://www.robinwieruch.de/machine-learning-javascript-
web-...](https://www.robinwieruch.de/machine-learning-javascript-web-
developers/)

Podcast:

\- [http://ocdevel.com/podcasts/machine-
learning](http://ocdevel.com/podcasts/machine-learning)

Courses:

\- [https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning)

\- [https://de.udacity.com/course/machine-learning-engineer-
nano...](https://de.udacity.com/course/machine-learning-engineer-nanodegree--
nd009)

\- [https://www.coursera.org/specializations/deep-
learning](https://www.coursera.org/specializations/deep-learning)

\- [http://course.fast.ai/](http://course.fast.ai/)

Books:

\-
[https://www.amazon.com/gp/product/B014X01SS0/](https://www.amazon.com/gp/product/B014X01SS0/)

\- [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)

\-
[http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

\- [https://www.safaribooksonline.com/library/view/deep-
learning...](https://www.safaribooksonline.com/library/view/deep-
learning/9781491924570/)

Math:

\- [http://www.fast.ai/2017/07/17/num-lin-
alg/](http://www.fast.ai/2017/07/17/num-lin-alg/)

\- [https://www.khanacademy.org/math/linear-
algebra](https://www.khanacademy.org/math/linear-algebra)

\- [https://www.khanacademy.org/math/statistics-
probability](https://www.khanacademy.org/math/statistics-probability)

\- [https://www.khanacademy.org/math/calculus-
home](https://www.khanacademy.org/math/calculus-home)

JavaScript ML:

\- [https://bri.im/](https://bri.im/)

\- [https://github.com/javascript-machine-
learning](https://github.com/javascript-machine-learning)

------
shrumm
tried fast.ai?

