The courses we're building over the next year, in the order we plan to ship them. Introduction courses come first; the advanced courses that build on them follow. Dates are estimates and shift as we build; free lessons ship as they're written.
Introduction to Machine Learning and AI
The paradigms and approaches of ML and AI: supervised, unsupervised, and reinforcement learning, plus deep learning.
Introduction to Python
Python from the ground up, taught inside an embedded coding workspace with notebooks, files, and a terminal.
Introduction to Mathematics & Statistics for AI and ML
The linear algebra, calculus, and probability that models are made of.
Introduction to SQL
Querying, aggregating, and transforming data — the language of every data team.
Introduction to System Design
Components, trade-offs, and the design canvas: reasoning about systems that hold up under load.
Python for Data Analysis
NumPy, pandas, and plotting: the day-to-day toolkit of analytical work.
Linear Algebra for Machine Learning
Vectors, matrices, and decompositions, developed alongside the models that use them.
Probability & Statistical Inference
Distributions, estimation, hypothesis testing, and Bayesian reasoning.
Databases & Data Modeling
How data is stored and shaped: schemas, indexes, transactions, and modeling decisions.
Deep Learning
Beyond the first neuron: convolutional networks, sequence models, and transformers, trained hands-on.
Optimization for Deep Learning
Gradients, convexity, and the optimizers that train modern networks.
Writing Production Python
Typing, packaging, profiling, and testing code that other systems depend on.
ML System Design
Feature pipelines, serving, evaluation, and monitoring for models in production.
Data Engineering Pipelines
Batch and streaming pipelines that feed models reliably.
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