
Ask HN: I'm 28. Is it too late to get started with AI/Machine Learning? - thakobyan
If no, what are the great resources for starters? Any tips before I get this journey going? Thank you.
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matchmike1313
It's never too late. Maybe apart from some age bias in the field (but that
most occurs closer to 40). I would suggest starting with some good foundation
in stats / modeling, this is an amazing book on that: An Introduction to
Statistical Learning by Robert Tibshirani and Trevor Hastie.

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shock
No.

I recommend the ML course by Andrew Ng (I did it on coursera) and the AI
course by Sebastian Thrun and Peter Norvig.

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mindcrime
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](http://ai.berkeley.edu/home.html)

This class:

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

This class:

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

This book:

[https://www.amazon.com/Artificial-Intelligence-Modern-
Approa...](https://www.amazon.com/Artificial-Intelligence-Modern-
Approach-3rd/dp/0136042597)

This book:

[https://www.amazon.com/Hands-Machine-Learning-Scikit-
Learn-T...](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-
TensorFlow/dp/1491962291/ref=sr_1_3?s=books&ie=UTF8&qid=1509309023&sr=1-3)

This book:

[https://www.amazon.com/Introduction-Machine-Learning-
Python-...](https://www.amazon.com/Introduction-Machine-Learning-Python-
Scientists/dp/1449369413/ref=sr_1_5?s=books&ie=UTF8&qid=1509309023&sr=1-5)

These books:

[http://greenteapress.com/thinkstats/thinkstats.pdf](http://greenteapress.com/thinkstats/thinkstats.pdf)

[http://www.greenteapress.com/thinkbayes/thinkbayes.pdf](http://www.greenteapress.com/thinkbayes/thinkbayes.pdf)

This book:

[https://www.amazon.com/Machine-Learning-Hackers-Studies-
Algo...](https://www.amazon.com/Machine-Learning-Hackers-Studies-
Algorithms/dp/1449303714)

This book:

[https://www.amazon.com/Thoughtful-Machine-Learning-Test-
Driv...](https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driven-
Approach/dp/1449374069)

These subreddits:

[http://artificial.reddit.com](http://artificial.reddit.com)

[http://machinelearning.reddit.com](http://machinelearning.reddit.com)

[http://semanticweb.reddit.com](http://semanticweb.reddit.com)

These journals:

[http://www.jmlr.org](http://www.jmlr.org)

[http://www.jair.org](http://www.jair.org)

This site:

[http://arxiv.org/corr/home/](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](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.

