Ask HN: How to self-learn ML? | Hacker News Reader

Ask HN: How to self-learn ML?

50 pointssidyapa18 comments

With a plethora of resources on google, Quora and HN, I would love to know :-

1. Detailed roadmaps for a beginner 2. Prerequisites and resources for every topic. 3. How you taught yourself Machine Learning.

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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

I have some ML introductory lectures on my YouTube Channel, https://www.youtube.com/channel/UC82zocd7ZWMSHe5uuPT4gSw?vie...

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

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).

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.

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.

Check out my study plan:. 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.

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.

There's an open CMU class from 2015 with lectures 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/

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

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

https://www.humblebundle.com/books/artificial-intelligence-b...

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

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

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.

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.

I'd second this, plus Jeremy and Rachael hang out on HN occasionally.

I've also worked my way through "Hands on machine learning with scikit learn and tensorflow" by Geron and have found it pretty approachable, up until tensorflow anyway.

But https://github.com/aymericdamien/TensorFlow-Examples helped with that.

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

http://lausbert.com/2018/01/14/the-shortest-introduction-to-...

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

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

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.

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-....

Podcast:

- http://ocdevel.com/podcasts/machine-learning

Courses:

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

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

- https://www.coursera.org/specializations/deep-learning

- http://course.fast.ai/

Books:

- https://www.amazon.com/gp/product/B014X01SS0/

- http://www.deeplearningbook.org/

- http://neuralnetworksanddeeplearning.com/