Reading signals
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Reading signals
Course catalog
Every ML/AI module is an independent course with clear prerequisites, dense practice, a portfolio artifact, and interview transfer.
Complete career program
Foundation, core ML, a chosen specialization, portfolio work, and interview preparation—without hiding the individual courses.
See how the path worksIndependent by design
25 of 25 courses
Build real programs from zero and learn to reason about code, not memorize syntax.
Move from general Python to reproducible analytical and machine-learning workflows.
Think in shapes, broadcasting, memory layouts, and vectorized transformations.
Convert messy tables into trustworthy analytical datasets without silent corruption.
Ask better questions, expose data problems, and communicate evidence without misleading charts.
Use geometry, vectors, matrices, and decompositions to reason about models.
Reason under uncertainty and distinguish evidence from noise.
Extract trustworthy training and analytical datasets and defend every join and aggregation.
Frame problems, establish baselines, train models, and make evidence-backed decisions.
Know whether a model is actually better—and whether that improvement matters.
Build transformations that are reproducible, leakage-safe, and consistent at inference time.
Improve strong baselines with disciplined optimization instead of blind tuning.
Backtest forecasts without looking into the future and connect accuracy to decisions.
Build candidate, ranking, and evaluation stages for personalized products.
Understand training dynamics well enough to diagnose a model that refuses to learn.
Build image systems that survive imbalance, shift, annotation noise, and inference constraints.
Model text, evaluate language tasks, and reason about tokenization and attention.
Turn an impressive demo into an evaluated, secure, and observable AI product.
Package, deploy, monitor, and safely change machine-learning systems.
Make and defend system choices under scale, latency, quality, and organizational constraints.
Practice the coding patterns most likely to appear in DS and MLE screens.
Choose, scope, build, review, and present portfolio projects that demonstrate real judgment.
Convert learning into credible evidence and perform across the US/EU hiring loop.
Deliver a production-minded ML system and defend it as you would in a hiring panel.
From your first SELECT to a defensible data platform