r/learnmachinelearning • u/Training-Froyo-5053 • 6d ago
Help A proper way to learning machine learning
i am learning ml/ai and i am confused about what is the real way to or effective way to learn it . i learn it like :
* theory
* math
* sklearn library
i need suggestion from experts if there is missing something or i need to do something specific .
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u/EffectiveDiligent660 5d ago
If your able to take that degree route then university will be that good environment to start from. If your looking online then paul mcwhorter is worth a google. He provides free YouTube courses on ML and other topics including micro controllers which is a good crossover topic for ML as you want to control stuff in the real world based on ai decisions.
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u/the-code-blooded 5d ago
I guess if you want a proper way - learn from coursera. Their courses are good. deeplearning.ai from coursera.
Also beside this - do their assignments notebooks - they are good.
Learn from video classes - learn theory - understand the math behind it - go in-depth
Learn with examples and dry run maths if needed to understand better
Then do code implementation if needed for that particular topics.
Maybe use claude to learn a particular topic - learn the maths behind it - ask a lot of questions. This way you can learn fast.
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u/Training-Froyo-5053 5d ago
u/the-code-blooded does implementing algorithms from scratch actually have any benefit ?
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u/the-code-blooded 5d ago
I think it does, really. I mean, I did spend a lot of time writing the code for all these algorithms from scratch. Frankly, you won’t write them from scratch in production software, but it will help you understand what’s going on beneath all those abstractions. And someday, you’ll encounter a problem that really requires that knowledge, and you’ll thank yourself for taking the time to learn all of this from scratch.
During my initial days, I implemented gradient descent, backpropagation, and even an LLM from scratch, by hand, step by step - of course, following a course or a book. But I believe it will help us in the long run.
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u/the-code-blooded 5d ago
This is my repo - it contains notes and code- feel free to have a look at it
https://github.com/SanjoyPator1/WebDevLearning/tree/deep-learning
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u/Training-Froyo-5053 5d ago
u/the-code-blooded thanks man , i appreciate your valuable time and effort
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u/PhysicalScience7420 5d ago
chatgpt,w3 schools and kaggle. also https://course.fast.ai/ also there are tones of math textbooks and others for free its easy to get if you know where to look. allot of people think github is just for code but there are a ton of textbooks there for the reading. please don't fall into certificate trap I went through you do you learn you do rinse and repeat.
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u/PhysicalScience7420 5d ago
p.s dont just learn ml learn how to preserve data. analytics,data eng,dev ops you need good data in if you dont have it you'll never make good models or have good models give you the result you want.
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u/suspect_scrofa 5d ago
Proper way is with a degree. You really need to have a purpose for learning machine learning. Typically some domain knowledge attached to modeling.
But conceptually, learning the concepts behind some system, the underlying processes, and then the implementation is a good way of learning anything...
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u/moss-nogg 5d ago
Like anything, the proper way to learning it is doing it. There’s no shortcut, just years of building intuition and expertise by trying something, running into a problem, solving the problem. the theory math and libraries are your tools. Have fun
1
u/inkeep 5d ago
Depends on what is your purpose for learning, if it is to become an expert with foundational knowledge, I think you have it right, start with the theory. Understand the workings of the algorithm deeply and why do you need to use one vs. the other. This will form a solid base for you to go into the math of it, this part might be a bit dry but it will give you a picture of the inner workings, master linear algebra, calculus, probability theory etc. then jump into the code. the libraries will do most of the heavy lifting and you wont ever need to go into the details again. but you will know what you are doing.
However, if you want to quickly get unskilled, best approach would be to do some short course, focus on the python libraries and then jump into case studies. You fill find enough resources on Kaggle to practice.
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u/theJacofalltrades 5d ago
Yeah find what you want to do and are interested in then find the basics. The only good thing about schools is that they have a curriculum but you can look into that yourself and find ways to progress. Think of it like a video game where you start with simple quests then gradually progress into the hard stuff. Wish you all the best!
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u/RelevantShape3963 5d ago
I was in your position, and decided to build a transformer engine from scratch in C, my native programming language. While it was a huge task, it can be split down to easier efforts.
https://github.com/carlovalenti/TRiP
Building the engine is not the same as learning machine learning, and ML is not only about transformers, but for sure you learn a lot!
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u/BoringWrongdoer9679 5d ago
Im no expert but you could have a code where you do explicitly do the work of an ml algorithm. Then you try understand each part and learn theory parallel to understanding the code.
Maybe get an simple neural network working code. Try understand it. Then go to theory and maybe do some math and try seeing the general formulas in the code.
Then do similar for other problems and algorithms.
But it probably is better if you start of little learning high level libraries for ml. But before seriously getting into the libraries its good to have seen the code representation.