Ask HN: Why there is no Codecademy for ML or AI? - allenleein
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scryder
Codecademy's model tends to be very poor for topics that are sufficienty
theoretically complex, as the turnaround time between exercise beginning and
conclusion appears to be about 5 minutes for non-project work.

Repetition/drilling for understanding and examination of knowledge by self-
testing are wholly absent.

Projects like "free code camp" can dodge this problem because the staff is
working for free as an open source project, but it would take a lot more 5
minute segments to teach someone linear algebra, the theory behind any type of
model, and the background information that is necessary to generate provably
compelling insight than companies like Codeacademy seem interested in
tackling.

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ssivark
0\. Doing ML requires an understanding of math. One gets less mileage from
making magic incantations w/o understanding underlying implementation,
compared with the mileage for front-end dev, as an example.

1\. Any non-trivial ML will require significant amount of computational power
(RAM/CPU/GPU). Who will subsidize that for participants? It's not sufficient
to have a laptop that can run a browser.

2\. Notice the proliferation of data science bootcamps. Once the
syllabus/infrastructure becomes streamlined (and cheap), I would expect there
to be more online offerings.

That said, there might currently be a market need for a syllabus based on
Kaggle or other freely available data sets, and free compute resources
provided on cloud platforms.

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bbennett36
There is tons of online tutorials for these things. Kaggle has good tutorials
with the test datasets

~~~
allenleein
Yes, I did my research but there is no such interactive tutorial online like
Treehouse or Codecademy. There are so many tutorials but none of it tells you
the whole path.

Here are the resources I found useful:

========================================== Advices from Open AI, Facebook AI
leaders

Courses You MUST Take: Machine Learning by Andrew Ng
([https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning)) /// Class notes:
([http://holehouse.org/mlclass/index.html](http://holehouse.org/mlclass/index.html))

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory
than the Coursera class but it is still relevant for
beginners.([https://work.caltech.edu/telecourse.html](https://work.caltech.edu/telecourse.html))

Neural Networks and Deep Learning (Recommended by Google Brain Team)
([http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/))

Probabilistic Graphical Models ([https://www.coursera.org/learn/probabilistic-
graphical-model...](https://www.coursera.org/learn/probabilistic-graphical-
models))

Computational Neuroscience ([https://www.coursera.org/learn/computational-
neuroscience](https://www.coursera.org/learn/computational-neuroscience))

Statistical Machine Learning
([http://www.stat.cmu.edu/~larry/=sml/](http://www.stat.cmu.edu/~larry/=sml/))

From Open AI CEO Greg Brockman on Quora

Deep Learning Book
([http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)) ( Also
Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep
learning engineering (except reinforcement learning). by Greg

2\. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263
(Stanford) ([http://ee263.stanford.edu/](http://ee263.stanford.edu/)) or
Linear Algebra (MIT)([http://ocw.mit.edu/courses/mathematics/18-06sc-linear-
algebr...](http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-
fall-2011/index.htm))

Neural Networks for Machine Learning — Geoff Hinton (Coursera)
[https://www.coursera.org/learn/neural-
networks](https://www.coursera.org/learn/neural-networks)

Neural Nets — Andrej Karpathy’s CS231N (Stanford)
[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287
(Berkeley)
[https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/)

Deep RL — John Schulman’s CS294–112 (Berkeley)
[http://rll.berkeley.edu/deeprlcourse/](http://rll.berkeley.edu/deeprlcourse/)

From Director of AI Research at Facebook and Professor at NYU Yann LeCun on
Quora

In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and
Statistics, and as many physics courses as you can. But make sure you learn to
program.

~~~
atarian
What does physics have to do with ML/AI?

~~~
kevinphy
"The Extraordinary Link Between Deep Neural Networks and the Nature of the
Universe" [https://www.technologyreview.com/s/602344/the-
extraordinary-...](https://www.technologyreview.com/s/602344/the-
extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-
universe/)

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coralreef
Udacity has a program for AI, self driving car engineering, as well as a
foundations program on deep learning .

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jfaucett
well dataquest.io is for data science but they seem to be going down that
track.

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eastWestMath
It's called an M.Sc.

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probinso
Insight datascience

