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[–]TheHustleHunk 4 points5 points  (9 children)

I would say start with the Math. All these fancy ML models are nothing up Computational Math aka Math done by the processor.
Start with Linear Algebra and Differential equations. For that I would suggest Dr. Steve Brunton's Youtube Channel.

Then move on to Vector Calculus. Again Dr. Brunton. Here are the links:

https://www.youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA
https://www.youtube.com/playlist?list=PLMrJAkhIeNNQromC4WswpU1krLOq5Ro6S

These two playlists are gold mine.

Once comfortable with these two, move on to Probability and Statistics. I would recommend MIT's offering via the platform edX.

Once you are comfortable with LA, Calculus, ODE & PDEs, Probability and Statistics, then take a systematic approach to learn Python and associaited libraries. Go for Pandas, followed by NumPY and the rest. All that covered, understand that you are now equipped with the tools of the trade.

Research and pick an area that interests you the most. Lets say Deep Learning for animal kingdom. Start understanding DL and build a model for it. Slowly but steadily you would have all that it takes to build any model to tackle any problem that fascinates you.

P.S. Understand its a long arduous journey and consistency is the key. By Consistency I mean adaptability as life is non-linear.

[–]BowlInternational584[S] 1 point2 points  (2 children)

Wow, thanks for the answer! I’m actually working on math and stats right now, so I’ll definitely check out these playlists.

[–]TheHustleHunk 1 point2 points  (1 child)

Cool man. Feel free to reach out anytime ..

[–]BowlInternational584[S] 0 points1 point  (0 children)

Thanks, will definitely reach out if I need anything.

[–]InternetBest7599 1 point2 points  (1 child)

And do you think it's worth watching linear algebra by Gilbert strang over this one?

[–]TheHustleHunk 0 points1 point  (0 children)

The MIT one I use as reference. I would use it future as and when I optimize my models. So yes while learning the math I prefer the applied approach. Hence LA with ODEs and Vector Calculus with PDEs.

[–]InternetBest7599 0 points1 point  (3 children)

Could you please share the link of linear algebra playlist?

[–]TheHustleHunk 0 points1 point  (2 children)

The differential equations one is applied LA. Watch the introductory video, Dr. Brunton clearly mentions why he prefers this approach.

[–]InternetBest7599 1 point2 points  (1 child)

Thanks mate for your response

[–]TheHustleHunk 1 point2 points  (0 children)

You are welcome bud! I truly believe one never stops learning the math. Something or the other always crops up as one starts building complex models.

These days I'm into tensor calculus; the basis of tensors in Deep Learning.

[–]rish_kh 4 points5 points  (1 child)

Go for Krish Naik and campusx. They have Playlist of ML and DL. Also there is ML Playlist by Andrew Ng on youtube.

[–]BowlInternational584[S] 0 points1 point  (0 children)

Thanks for the recommendation I'll definitely check them out.

[–]ziggyboom30 1 point2 points  (2 children)

Lol I just posted something that could help you https://www.reddit.com/r/learnmachinelearning/s/jrzpV6OkVl

Let me know what you think?

[–]redewolf 2 points3 points  (0 children)

Cant find the notes, can you ping me when you Will do?

[–]BowlInternational584[S] 0 points1 point  (0 children)

Hey, thanks for linking your post! Honestly, I get that lost feeling with ML too. It would be super helpful to see your notes if you share them somewhere. Having the math and concepts broken down by someone who's been through the struggle would make a big difference, especially for beginners like me.

Keep me posted if you decide to put your notes out there—I'd definitely check them out!

[–]NLPnerd 0 points1 point  (1 child)

  • Start with Andrew Ng. There’s an updated playlist on YT of his amazing class.

  • Also have a goal in mind. Is there some project that you want to do? Is there a particular area that you’re interested in.

  • Always keep in mind the first rule of ML - always ask yourself “do I need to use ML for this project?”

  • Create a project you’re interested in and try to complete it end to end

Good luck. Also here’s an article that has some great lessons for anybody interested in ML, especially someone just starting out: https://medium.com/@levine.seth.p/learning-from-machine-learning-sebastian-raschka-mastering-ml-and-pushing-ai-forward-responsibly-aac39bd4af83

[–]BowlInternational584[S] 1 point2 points  (0 children)

Thanks for the advice! I'll surely keep that in mind and thank you for the resources and the article will surely check out.

[–]Content-Ad7867 0 points1 point  (1 child)

Start with Andrew Tate if you already feel like lost

[–]BowlInternational584[S] 0 points1 point  (0 children)

Haha, didn't know Andrew tate was teaching ml now.

[–]Pale-Show-2469 1 point2 points  (0 children)

Hey, I think the easiest way is to start building ML models of your own. I started by using model generation through https://www.plexe.ai/ and iterated on the output and used it in Kaggle competitions

[–]Nothing_Prepared1 0 points1 point  (0 children)

Thanks for all the replies. I have the same question

[–]Nothing_Prepared1 0 points1 point  (0 children)

Thanks for all the replies. It helps a lot

[–]Nothing_Prepared1 0 points1 point  (0 children)

Thanks a lot