
Ask HN: How to self-learn ML? - sidyapa
With a plethora of resources on google, Quora and HN, I would love to know :-<p>1. Detailed roadmaps for a beginner
2. Prerequisites and resources for every topic.
3. How you taught yourself Machine Learning.
======
tomerbd
Unfortunately, there is simply too much information in ML, I found that it's
not just like learning a new programming language where you can scope it to a
certain size or amount of studying, the way I try to deal with it is
CheatSheets, so I created my own below:

Check out my ML and DataScience CheatSheets here: [https://tomer-ben-
david.github.io/datascience-cheatsheet](https://tomer-ben-
david.github.io/datascience-cheatsheet)

I have some ML introductory lectures on my YouTube Channel,
[https://www.youtube.com/channel/UC82zocd7ZWMSHe5uuPT4gSw?vie...](https://www.youtube.com/channel/UC82zocd7ZWMSHe5uuPT4gSw?view_as=subscriber)

I try to keep all material concise for a clean slate learner.

------
John_KZ
I would suggest going to university. Anything else is a waste of time if
you're looking for employment.

The only exception would be if you're an employee (programmer) of a large firm
that's willing to train you and put you in a position to use your skills. But
if that was the case you wouldn't be here. Don't spend months of your time
self-training because nobody will hire you without hard qualifications.

Also ML is a very large and diverse field, with many different sub-categories.
What you learn from online courses depends on the course. Most of them are
essentially just training videos that teach programmers how to use a certain
library. If you really want to learn ML, browse for graduate programmes in
universities you can attend. If you don't have an undergraduate degree, go get
one. If you only want to learn ML as a hobby with no prospects of getting
employed, try studying from various online courses (ie on MIT or coursera
etc).

------
jnord
I did Coursera's "Introduction to Machine Learning" by Andrew Ng back in 2013
and loved it. Great tutor, good course material - and it looks like Coursera
is still offering this course though I am not sure if it is still free. The
course is language-agnostic and uses Octave (an open-source Mathlab clone) for
assignments and examples.

~~~
rejschaap
It is a very good introduction course, I did it as well in 2013. You can still
do it for free, but you will not receive the Coursera certificate on
completion. To receive the certificate you need to pay $79.

------
desio
Check out my study plan:. [https://github.com/desicochrane/data-science-
masters](https://github.com/desicochrane/data-science-masters)

Its still evolving, but the earlier parts are pretty comprehensive and
resources have been over a year in curation.

~~~
ziikutv
Amazing. I am looking for something like this for Engineering (Aero/Mech). I
think the first part has a lot of parallels so that's great.

------
hackermailman
There's an open CMU class from 2015 with lectures
[http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml](http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml)

It assumes you have a working knowledge of probability, linear algebra,
statistics and algorithms at the undergrad level but the recitations (also
open) are designed to fill these gaps. From there you would start going
through the latest journals/papers in ML. There is also a practical data
science class that's open with some ML content
[http://www.datasciencecourse.org/lectures/](http://www.datasciencecourse.org/lectures/)

If you can get the entire playlists from youtube before you start watching
because often these resources disappear

------
pepsi
Pay $15 for a decent amount of starter material in this Humble eBooks Bundle.

[https://www.humblebundle.com/books/artificial-
intelligence-b...](https://www.humblebundle.com/books/artificial-intelligence-
books)

------
niko001
I prefer learning by messing with existing examples rather than watching
YouTube videos or reading books, so I created a directory of ML projects that
1) have 'interesting' outputs, 2) are well documented and 3) open source:
[https://ml-showcase.com](https://ml-showcase.com)

------
jordancampbell
I'd highly recommend course.fast.ai - it's focussed on deep learning, but is
designed for beginners to get to a production stage.

~~~
shriek
Does deep learning encompass all ML though? While I agree it can be used for
numerous tasks I don't think starting ML with deep learning is such a good
idea.

~~~
jordancampbell
Deep learning is easy and popular - it's just as good a starting point as
anything else. No one would argue that linear regression isn't a good starting
point, but only because it's not fashionable to dismiss linear regression.

------
Lausbert
You might start with my shortest introduction to machine learning ;)

[http://lausbert.com/2018/01/14/the-shortest-introduction-
to-...](http://lausbert.com/2018/01/14/the-shortest-introduction-to-machine-
learning/)

------
aaron-santos
I'm learning the material myself. Shoot me an email. I'd be happy to share
notes.

~~~
gilbertmpanga12
can i have as well please gilbertmpanga.gm@gmail.com

------
hmm_really
There is an assumption with some of these responses that you want to learn ML
for career progression.

If so, are you really interested in ML or do you just think its the hot
bandwagon of the moment which you want to jump on to get ahead? If that is the
case, I'd suggest that perhaps that is a bit obvious and to identify something
else that is less hyped and mainstream. Perhaps something which you can get
ahead of the crowd on and ideally, have genuine interest in.

------
rwieruch
Just recently I have written a "Machine Learning for Web Developers in
JavaScript" blog post [0]. If you or someone else is a web developer, it might
be interesting. 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 for myself to learn about ML below.

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

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://eu.udacity.com/course/machine-learning-engineer-
nano...](https://eu.udacity.com/course/machine-learning-engineer-nano..).

\- [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..).

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)

