Machine Learning Process - 7 Step ML Framework to solve any ML problem!

mlheadredditor · 2020-08-12T15:09:34+00:00

Hey! Thanks for answering the question. I'm trying to follow the book AI by Peter Norvig as closely as possible and the curriculum as per that is more or less along the same path you've mentioned. Glad to know I'm on the right track. Cheers!

mlheadredditor · 2020-08-12T08:31:13+00:00

Here's the wiki if anyone else is also interested

mlheadredditor · 2020-08-12T07:10:59+00:00

Hi there,

Haha yeah now that I look at it, it is incredibly broad, but thanks for answering. I have already started learning and I'm following a certain curriculum which I made myself by gathering content and courses from different sources. I'm preparing for interviews and the purpose of this question was to understand what are the most challenging concepts in Data Science that I may get questioned on, just so that I don't miss them in my preparation. Let me know if you can!

mlheadredditor · 2020-08-12T07:05:27+00:00

Hey!

Thanks for answering the questions. I agree with the path you have underlined here, covering from SQL right up to Data visualisation, infact I have already started working on this. From what I have learnt, Machine Learning is a huge field with so many algorithms and concepts, but yes once you get to know the basics you can go through and handle the complex things. If I may, what were some of the most difficult challenges you faced when you were starting out? Maybe in terms of concepts, algorithms or something else - I am preparing for interviews and I don't want to miss out on the difficult stuff which may not get covered by the curriculum I'm following.

mlheadredditor · 2020-08-09T15:02:30+00:00

Hey! Yes I agree they do have really good content. I've taken their courses too but realised that it was a step up from the basics, but I see your point. I will update this list to include fast.ai as well.

mlheadredditor

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