HN Academy

I took these courses from Georgia Tech via OMSCS but they are also on udacity.

https://omscs.gatech.edu/cs-7641-machine-learning

https://omscs.gatech.edu/cs-7642-reinforcement-learning (I took this before ML but its supposed to come after. There is some overlap. Probably my favorite graduate course.)

https://omscs.gatech.edu/cs-7646-machine-learning-trading (IMO not amazing)

Much more basic (took this before OMSCS):

https://www.udacity.com/course/intro-to-machine-learning--ud...

I'm sure there are many more.

⬐ jay3ss

What about machine learning for trading didn't you like?

⬐ deskamess

I found ML4T to be really accessible and fun. Cannot say the same for ML.

http://www.fast.ai/

https://www.udacity.com/course/intro-to-machine-learning--ud...

https://agi.mit.edu/

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

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

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

⬐ 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/

[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...

What? No. Why in the world do people even ask this kind of question. To a first approximation, the answer to "is it too late to get started with ..." question is always "no".

If no, what are the great resources for starters?

The videos / slides / assignments from here:

http://ai.berkeley.edu/home.html

This class:

https://www.coursera.org/learn/machine-learning

This class:

https://www.udacity.com/course/intro-to-machine-learning--ud...

This book:

https://www.amazon.com/Artificial-Intelligence-Modern-Approa...

This book:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

This book:

https://www.amazon.com/Introduction-Machine-Learning-Python-...

These books:

http://greenteapress.com/thinkstats/thinkstats.pdf

http://www.greenteapress.com/thinkbayes/thinkbayes.pdf

This book:

https://www.amazon.com/Machine-Learning-Hackers-Studies-Algo...

This book:

https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driv...

These subreddits:

http://artificial.reddit.com

http://machinelearning.reddit.com

http://semanticweb.reddit.com

These journals:

http://www.jmlr.org

http://www.jair.org

This site:

http://arxiv.org/corr/home/

Any tips before I get this journey going?

Depending on your maths background, you may need to refresh some math skills, or learn some new ones. The basic maths you need includes calculus (including multi-variable calc / partial derivatives), probability / statistics, and linear algebra. For a much deeper discussion of this topic, see this recent HN thread:

https://news.ycombinator.com/item?id=15116379

Luckily there are tons of free resources available online for learning various maths topics. Khan Academy isn't a bad place to start if you need that. There are also tons of good videos on Youtube from Gilbert Strang, Professor Leonard, 3blue1brown, etc.

Also, check out Kaggle.com. Doing Kaggle contests can be a good way to get your feet wet.

And the various Wikipedia pages on AI/ML topics can be pretty useful as well.

For anyone interested in SVMs (and other introductory Machine Learning concepts), Udacity's intro course is really good: https://www.udacity.com/course/intro-to-machine-learning--ud...

⬐ yamaneko

This class from MIT taught by Patrick Winston is also a great resource: https://www.youtube.com/watch?v=_PwhiWxHK8o

At the end of the class, he also gives some historical perspectives, like how Vapnik came up with SVMs.

These 3 are the most well know and well regarded 0-to-hero type intro courses online, and high-school math is sufficient to follow along (but pick only one and go start to finish!):

* https://www.udacity.com/course/intro-to-artificial-intellige... (by Peter Norvig - director of research @ Google & Sebastian Thrun - lead dev of google self driving car and founder of google x, now at gerogia tech uni) - great if you want a more "deep thinking" style intro to AI

* https://www.udacity.com/course/intro-to-machine-learning--ud... (Sebastian Thrun & Katie Malone - former physicist and data scientist great at explaining stuff so that anyone can grok it) - great if you want a more "down to earth" engineering style intro with simple clear examples

* https://www.coursera.org/learn/machine-learning (Andrew Ng @ Stanford & chied scientist at Baidu, former Google researcher) - great if you want a "bottom up", from math, through code/engineering, with less fuzzy big picture stuff - this is a great intro, even if Andrew Ng is less of a rock-star-presenter, if you want to start from math details up take this one!

Oh, and kaggle: https://www.kaggle.com/ . If you get stuck on anything, google the relevant math, pick up just enough to have an intuition and carry on.

You're still in college so you have plenty of time to learn well the required math, it's better to get a broad picture of the field ASAP imho! Then when you'll take the math classes, you'll already have "aha, this feels my gap about X and Y" and "aha, now I get why Z" and you'll really love that math after you already know what problems it solves!

(PS if you're less of a "highly logico-intuitive" person and more "analytical rigorous thinker" instead, just ignore my last paragraph and focus on the math, but try to get some deep intuition of probability along the way)

⬐ CN7R

I'll try to do the coursera course on ML -- last time I tried I got swamped by schoolwork. Thanks for the suggestions.

Or how does it compare to udacity's intro to machine learning? https://www.udacity.com/course/intro-to-machine-learning--ud... it was recommended in https://medium.com/learning-new-stuff/machine-learning-in-a-...

udacity free machine learning course is a nice way to get the basics https://www.udacity.com/course/intro-to-machine-learning--ud...

Udacity also offers a free course on machine learning: https://www.udacity.com/course/intro-to-machine-learning--ud... You will start with mini projects and will also work towards a final project: "searching for signs of corporate fraud in Enron data"

⬐ tixocloud

Thanks! That could actually be my next step.

> Please don't be like Udacity

Huh? Udacity has numerous free courses. They've never made a free course non-free. Any new paid course is developed with new material, and they're still churning out free courses.

Here's one of the gems from the early days of Udacity - Peter Norvig's Design of Computer Programs: https://www.udacity.com/course/design-of-computer-programs--...

And here's one of the early machine learning courses: https://www.udacity.com/course/intro-to-machine-learning--ud...