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.
6 módulos • 20 lecciones
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 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.
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.
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.
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.
Master data visualization using Matplotlib and Seaborn. Learn to create compelling visualizations from basic plots to advanced statistical charts. Practice with real datasets and build visualization skills for data science, analytics, and business reporting.