Master machine learning from the ground up. Learn supervised and unsupervised learning, build models with scikit-learn, and understand the intuition behind algorithms like linear regression, decision trees, and neural networks. Hands-on Python exercises with real datasets.
13 módulos • 35 aulas
Understand the machine underneath modern AI without writing a line of code. Learn what a neuron does, why weights are the only place a model stores knowledge, how layers build their own concepts, and how training actually works through loss, gradient descent, and backpropagation.
Master the probabilistic foundations of artificial intelligence. Learn probability fundamentals, Bayes' theorem, distributions, expected value, maximum likelihood estimation, and how AI systems handle uncertainty to make predictions.
Master the calculus that powers machine learning. Learn derivatives, partial derivatives, the chain rule, gradients, gradient descent, loss functions, and backpropagation — the essential math behind how models learn.
Master linear algebra through the lens of artificial intelligence. Learn vectors, matrices, dot products, eigenvalues, and tensors by seeing exactly how they power neural networks, transformers, embeddings, and other AI systems.
Bridge the gap between AI curiosity and AI understanding. Discover why linear algebra, calculus, and probability are the three pillars of artificial intelligence, see how each one powers real AI systems like neural networks and LLMs, and get a clear learning path to master them all.
Master the art of chaining AI prompts and building sophisticated workflows. Learn to design multi-step AI pipelines, handle errors gracefully, implement branching logic, manage context, and build production-ready AI workflows for research, content creation, and code generation.