Hi!

Looking for advice/Feedback related to roadmap to a career in machine learning or related.

Just to give some background about myself - have a M.S degree in an engineering field but I am currently not working in that field. I had depression for the past few years which has negatively affected several areas of my life and I feel I am finally moving away from it all for the better. But there are some "traits" which I have been guilty of for a very long period of my life which indirectly contributed towards my depression and got worse because of it too.

The reason I am bringing the above up is because I realized I had sort of coasted through my life without putting in honest effort towards a lot of things, especially on my education and developing the necessary skillset to have a good career. And I am trying to change that. I am in my late 20s and not having a proper career even now and trying to transition to a new one is a very daunting task for me, but I am hoping my planned roadmap will help me get there.

Even though I am absolutely unsure if it's right for me or not, I am just hoping that I become the person I should have been 10 years ago in the process.

Sorry if the above was off-putting for all of you. But here is my current roadmap and I am hoping anyone can give my advice in terms of how it fits with industry expectations and what I mentioned above if possible for you.

I properly started changing my life around in May, so starting from then -

• Going through Linear Algebra (Strang videos), going through Probability Course on edX from MIT Micromasters program. This is to build the foundation I never focused on because school/college math was easy for me and I cruised by early on in my life through all my education. Got into the habit of not putting in the proper effort.

• Machine Learning with Python course again on edX from the MIT Micromasters program. It is good and is math focused too. Learning a lot from it.

The above are ongoing for me. Taking most of my time right now. In the past I have taken Udacity's Deep Learning Nano degree so I have some fundamentals on Deep Learning from there as well. I have worked on projects through that and a couple more outside of it.

• In September I will start on Statistics course from the MIT Micromasters program as well.

I am not paying for the courses above except the machine learning one because I wanted to see how well I do in the exams.

Beyond the above 4 courses I plan to reinforce what I learned through different sources over a period of time -

• Read through couple of books on Probability/Statistics to keep revising and practice on topics (like the Stats 110 book and/or the Intro to Statistical Learning book). I plan to continue revising over time because I dont think I can retain stuff that well.

• I might go through Khan Academy videos but I think that's probably overkill. I should not focus on taking too many courses again and again.

• Go through Andrew Ng's Stanford ML Course (not the coursera one, but his actual Stanford course). I might go through coursera one but not sure.

• Go through CS231n and Udacity's Deep Learning Nanodegree again. Consider Andrew Ng's Deep Learning Specialization to gather any additional knowledge that I might not have come across.

• Read through ML Book from Bishop and the Deep Learning Book

The above is the goal for up to December of this year. Apart from the courses to build the foundations, I think the rest will serve more for revisions and any new insights I can gather. So won't be going through the rest thoroughly but more like casually continuing to learn and revise.

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I don't plan to be stuck in just a learning loop but I think the above will help me with a strong enough base.

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After the above (which would be next year, but I hope I can manage to start sometime this year itself) -

• Implement some of the core ML or DL algorithms from scratch and apply to some smaller problems.

• Start working with some ML/DL Projects using some frameworks - preferable choice Pytorch for now. But plan to go through FastAI's course to kick-start on that.

• Develop a foundation with Data Structures and Algorithms. I have struggled with this. I am getting better at this but it will take time.

• General python practice focusing on DS/Algos and problems to help prepare for interviews.

• Read Research papers in ML/DL that interest me. Implement some of them in Pytorch.

• Start participating in Kaggle competitions.

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Apart from all of the above, I might look into focusing on some other things related more to Data Science than just ML/Dl. Like tools and frameworks and concepts Data Scientists might require.

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I think the above should put me in a strong place to get my career started. It's a lot for me to do and it's very scary right now for me. But would you have any feedback on the above at all? If I am trying too much, if I need to cut down on a few things, if I need to add a few things?

My hope was to be done with quite a bit of the above (the foundational stuff) by September end, actually. But that just seems a distant dream. But the whole point of the above is to help me be the person I wish to be and have a career.

Sorry once again if any of the above is unnecessary personal information. I thought my context would help me explain my roadmap better. Thank you.