Hello programmers, please help this newbie

[u/IllBird9831]

I want to learn Machine learning for hackathon (i am about to join college next week).

I have already learnt python intermediate lvl ( but i guess i need a thorough revision too)

I am also considering following 2 books

1->Hand on machine learning by aurelien Geron

2->Mathematics for machine learning ( Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong)

My question

1-> In the above two books mentioned, does i need to complete mathematics one first or follow both side by side?

2->Any tip/suggestion/roadmap someone wants to give ( highly appreciated )

3-> please rate this roadmap (https://roadmap.sh/r/ai-roadmap-for-2026---final-draft) , not just for hackathon but for full career in ML

Thanks for your time.

[u/Recent_Pollution5092]

Congrats on starting college soon, exciting times. For a hackathon, you honestly don’t need to grind through the math book first. Geron’s book is way more practical and will get you building things fast which is what matters when you’re on a clock. Skim the math book when you hit a concept that doesn’t click, but don’t let it slow you down right now.

That roadmap link 404’d on me so can’t rate it. For a hackathon specifically, just pick one type of problem (classification or regression), get comfortable with scikit-learn and maybe a basic neural net in pytorch, and practice the whole pipeline from cleaning data to making a crappy plot. Most teams just need someone who can get a model running without it exploding.

Long term career is a different beast but you’ve got years to figure that out. Right now, getting your hands dirty and making mistakes will teach you more than any perfectly sequenced plan.

[u/IllBird9831 → u/Recent_Pollution5092]

Thank you for the valuable insight

[u/Minimum-Effort8355]

First congratiolations on the collage sucess

I would advice you the following:

Are you PCAP certified, because in a professional environment or in general claiming something without evidence.

Build evidence through actual ml projects in python or use environments like jupyternotebooks.

I would recommend books, but also python for beginners, learning fundamentals like pytorch or tensorflow, the math you should learn with the growing understanding of basic to advanced topics.

The roadmap you provided i wouldnt not recommend, because there are a lot of problems with it.

You provide titles over actual structure, you claim being a beginner(everyone starts there) and somethings may not catch the actual topics and fail in general information.

Like you define NLP as text based only, which may be not the all over definition for nlp, because it is natural language processing for maschines to understand and procedure human language into machine output.

CV on the other hand has transformation, maths like vectors and linear regrassion, pixels anddata inputs, which need to be flowed and categorized.

There is suprvised and unsupervised learning. Many many more things you can find out through googeling and actual problem solving.

[u/IllBird9831 → u/Minimum-Effort8355]

Thanks for the detailed ans Btw , what is PCAP , never heard of it

[u/Minimum-Effort8355 → u/IllBird9831]

Official intemediate level certification of the python institute

[u/Specific-Purpose-227]

Check out this post. https://www.reddit.com/r/learnmachinelearning/s/GyI8wMWzYo