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.
6 módulos • 18 aulas
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.
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 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.
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.
Learn to leverage Claude AI for effective code review in 30 minutes. Master prompts for finding bugs, security vulnerabilities, and refactoring suggestions with hands-on practice.
Finally see how AI, machine learning, deep learning, and generative AI fit together. Learn where rules end and learning begins, what the "deep" in deep learning actually changes, when simpler machine learning still wins, and how to place any real product on the map. No code, no math.