Stuck in Learning

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I know Python and SQL, and Basic Mathematics,After that I’m Stuck, I really Wanna Learn Machine Learning but is it Important to Know Data Analytics, Data Engineering Before that ? If someone from Deep Learning Team can Guide me it’s so Overwhelming

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Hi @J_Sate,

Short answer: no. Data analytics and data engineering are not prerequisites for machine learning. They are neighbouring careers, not earlier rungs on the same ladder. Python + SQL + basic maths is already enough to start the Machine Learning Specialization today. You are not missing a step. You are just staring at the whole map at once, and the map is not the path.

A path that works from where you are

  1. Machine Learning Specialization (Andrew Ng, DeepLearning.AI & Stanford Online), three courses. Start here. It assumes basic Python and introduces the maths as you need it, so you don’t need to “finish maths” first.
  2. Deep Learning Specialization, if neural networks turn out to be the part that grabs you after step 1.
  3. Mathematics for Machine Learning and Data Science Specialization, taken alongside step 1 rather than before it, and only for the topics where you feel the gap (linear algebra, calculus, probability & statistics). Learning maths on demand beats learning it speculatively, because you retain far more when you already know what it is for.

Then pick a direction based on what you actually enjoyed, not on a checklist:

  • Liked deploying and scaling things? Look at Machine Learning in Production (MLOps). This is where a bit of data engineering becomes genuinely useful, and by then you will know exactly which bit.
  • Liked digging into data and communicating findings? Analytics-flavoured courses fit here. Also optional, also later.

On feeling overwhelmed

That feeling usually comes from trying to sequence every skill in the field before starting any of them. Two things that reliably help:

  • Commit to one specialization and ignore everything else until you have finished it. Roadmap-shopping feels productive but isn’t.
  • After Course 1, build one small end-to-end project on a dataset you personally find interesting. Load it, clean it, train something, evaluate it honestly, write up what you found. One scrappy project teaches you more about what you are still missing than any roadmap will, and it turns “am I ready?” into a concrete list of things to look up.

Your SQL and python background is a real asset, by the way. A lot of people arrive at ML unable to get their own data out of a database. You are further along than you think.

Welcome, and please come back to the forum when you hit a wall in a specific course. That is what we are here for.

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Thanks for the Reply :folded_hands: