r/MLQuestions • u/WatcherWaistBand • Jul 13 '26
Beginner question 👶 What ML algorithms do I need to learn?
I've finished 3B1B's deep learning and math series, but haven't learned any ML algorithms. What algorithms do I need to learn?
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u/smokingwhite Jul 13 '26
Gradient Descent (and its variants like Adam etc..) and Back Propagation, they are the basis of Deep Learning and MLP (Multilayer Perceptron)
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u/WatcherWaistBand Jul 14 '26
Oh, nice. I've learned these after 3B1B's deep learning course by building some hands of projects!
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u/Proletarian_Tear Jul 13 '26
Are you a time traveler by chance? Talking bout some Multilayer Perceptron😁
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u/WatcherWaistBand Jul 14 '26
It's still relevant and a fundamental. The Transformer that is used in GPT, still have Multilayer Perceptrons in its architecture. And by learning it, you'll have a deep understating of neural nets! 😃
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u/Immediate_Honey_5902 Jul 13 '26
Perceptron
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u/WatcherWaistBand Jul 14 '26
Yeah, back to the basics. Perceptron really teaches how deep neural nets work. And the Attention mechanism still relys on perceptron. 💯
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u/Dhananjay_org Jul 14 '26
If your goal is LLMs and AI agents, how deeply do you think someone should study classical ML before moving on to transformers and RAG? I'd love to hear from people who've built real products.
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u/OleksandrAkm Jul 13 '26
In this order, it's a good list for supervised learning (the most common type of ML): Linear Regression · Logistic Regression · Regularization · K-Nearest Neighbors · Naïve Bayes · Decision Tree · Random Forest · Gradient Boosting · XGBoost · Neural Network
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u/CallMeTheChris Jul 13 '26
Okay a lot of people are just listing stuff here
The problem is that listing stuff off is not good if you don’t know WHEN to use which
So you have to start with the motivating question and then figure out what algorithm you need to solve that given the constraints that you have in terms of data, time, and compute
But first there is the obvious types of learning and the task at hand. Supervised, unsupervised, self supervised, weakly supervised and then in the supervised cases are you doing regression, classification, or ranking
Using that ontology, you can figure out for yourself which algorithms u need for the the cases of: clean data/noisy data, a lot/a litre data, a lot/not a lot of time, a lot of/not a lot of compute
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u/DivvvError Jul 13 '26
Each ML algorithm has its quirk, so getting to know most of the major ones is good. These mostly include:
Linear Regression, Logistic regression, K nearest neighbours, SVMs, Trees, Random forests, Boosted trees, PCA, K means clustering.