Machine Learning Foundations
Real intuition for how machines learn from data — models, loss, gradients and generalization.
Go beneath the chat window. See how text becomes tokens and vectors, how attention lets a model use context, and how training turns a next-token predictor into a capable assistant — then apply that understanding to prompting, retrieval and evaluation.
Comfort with basic programming ideas. No machine-learning background is needed — every concept is built from first principles.
Real intuition for how machines learn from data — models, loss, gradients and generalization.
Neural networks from the ground up — neurons, backpropagation, optimizers and the techniques that make deep models train.
How agents learn by trial and error — bandits, Markov decision processes, value functions and Q-learning.
We can combine Generative AI & Large Language Models with other courses into a learning path built around your team’s roles and goals.