Machine Learning, resources

Machine Learning Self-Study Track

I started with Machine Learning a while back and had a slightly hard time getting help from the local community. The reason was mostly because the Machine Learning community in general is way behind the state-of-the-art in industry and research. This is true for almost all fields nowadays but with Machine Learning, the issues are more pronounced due to the recent fast-paced developments in the industry.

On the other hand, once you know what to study, things are much easier than many other fields such as security. Here I would outline the plan I followed to get to where I am (which isn’t too far ahead but still a little better than what most people know, IMHO).

So, here’s my guide for getting started with Machine Learning self-study.

  1. Start with Andrew Ng’s Coursera course — Machine Learning. That’s the advice almost everyone seems to give — and it’s a great advice. The Coursera course is completely basic and eases you in the field with little pre-reqs and not much depth. Be careful though: do not think after completing the course that you are an expert in Machine Learning. It misses quite a few areas and the skills needed to be above average. It does get you started with practicals so you are likely to think you’re already done after finishing the course.
  2. So, after you complete the courser in its entirety — including the assignments — I suggest you start with Prof. Nando de Freitas’ undergrad course.  This is a much more detailed course and would get you a very different view of ML than traditional outlines. Of course, you might have to brush up on your Probability, Calculus and Linear Algebra. You can’t really do anything without these three.
  3. For the above three, I suggest the following courses:
    1. Probability: Probability for Life Sciences by UCLA’s Math Department. You can find videos for this easily.
    2. Calculus: I strongly suggest you go with Virtual University Pakistan’s Calculus-I course by Dr. Faisal Shah Khan. It’s a great course but it’s in Urdu. If you don’t know Urdu, you can find your own series. Please let me know in the comments about great resources for this.
    3. Linear Algebra: Of course, this can only be done with Gilbert Strang’s Linear Algebra course from OCW.
  4. After that, you can start with the grad course and the second grad course by Prof. Nando de Freitas. Both have very detailed video lectures.

Of course, you also need to work with tools other than Matlab. I strongly suggest the python PyData stack. The full list would be:

  1. Python PyData full stack (plus go through their yearly videos as well)
  2. Theano
  3. Torch
  4. Keras

That’s what I have till now. I might add more when I know more inshaallah.

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research, Writing

Writing Better English — Avoid Very

I return with a minor post after another long break. This time, it’s about writing better English. Now, this isn’t humblebragging but I cannot be considered excellent at English writing — at least not by native standards. English is not my first language and I haven’t had much formal English education. I have, however, read a lot. Even if my English is not good, I can still point out some tips shared by experts.

Here’s the first one of those shared by Amanda Patterson on Writers Write. It’s a list of 45 words you can use to put emphasis on words without using the word “very”. I found it refreshingly helpful.

Bear in mind though that you cannot just go ahead and use a word without looking up its usage examples. Some words might have negative connotations even though the dictionary meanings look positive. For example, if you use the word ‘adequate‘ to describe someone’s work, they might be offended even though the dictionary meaning is that of acceptable quality.

p.s. After writing this, I searched for the word “very” and found two instances where I had used the word myself. I replaced it with better alternatives.