If you are thinking of starting ML, without a doubt Andrew Ng’s Course on Coursera. is the best place to start.
However, a couple of things below that should ease your journey.
- Make sure you complete at least 4 Weeks. The first 2 assignments are the mountain that you must scale before witnessing the beautiful horizons of ML.
- Do not skip any videos/lectures (I tried to act smart and tried; Let’s just say, not one of my brightest ideas…).
- Do not shy away from watching videos again and again(and again), if required. Everything you need is right there, in the videos.
- Use Emacs or Sublime Text as an editor (I spent quite a bit of time setting up the ‘ideal environment’ only to later use Sublime Text. (In the event that you discover a better alternative, please share.).
- If you think you lack the fundamentals to get this right, NOW is the time to get them (Looking back, Sleeping through that lecture on probability was not cool). The good news is you are smarter now with more resources at your disposal. It only takes 10 minutes to get each of the fundamentals correct.
- It is OKAY to reverse engineer for the first 2 weeks and then try again. There were times when I did not get to the solution directly. I saw the solutions on Github. With that as a reference, reverse engineered it. (Just make sure you re-implement them later on and are able to explain what has been done.)
- Octave-CLI is your ally, trust it; use it. (You’ll know this once you complete installation in Week 2)
- Get peers!! Get someone to discuss. With so many equations messing around, I can’t stress enough the importance of having someone to discuss them with. Start in pairs (get someone in the same boat as yourself); that should really help reinforce your learning.
- If the speed of videos is slow for you, watch videos at 1.25x or 1.5x.
- Don’t hush that inner voice that whispers, ‘You really did not get that, did you?’. Instead, embrace it.
- If you feel lost, you are on the right track(that implies you understand a tiny bit of it and are questioning the rest). The transition from an expert programmer to a grad student surely takes a toll.
- This is the most fundamental course there is which completely covers basics of ML (AFAIK). So, yes, you’ll have to get through it.
- Discuss, Explain and Talk ML. Nothing will concretize your understanding other than explaining it to someone else.
- If at some point while watching lectures, you feel that you are not following, PAUSE the video right there, go back and start over. Don’t, seriously DON’T, be in a hurry to finish the video up. Take your time and really understand what’s going on in there. Or it will come back and bite you later.
- Take pen and paper! Solve the algorithm with a very small dataset manually on paper. It really helps in understanding whats happening to the data and how the algorithm is working.
That’s it, folks.
Happy Machine Learning :).
[Image subject to copyright by Coursera.]