AI & Machine Learning
Start with the foundations, then choose machine learning or AI engineering.
Foundations
Learn Python, probability, and basic machine learning before choosing a track.
- 01
Python for Data Work
Turn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
Open courseBefore thisNo prerequisite courses - 02
Probability and Statistical Reasoning
Build probability from cases, then use it to reason about repeated observations and evidence.
Open courseBefore thisPython for Data Work - 03
Classical Machine Learning
Take one prediction problem through data cleaning, modeling, deployment, and monitoring.
Open course
Machine learning
Model, evaluate, and decide: the ladder behind data scientist and ML engineer interviews. Follow each course’s prerequisites; the later ones are not strictly sequential.
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Feature Engineering
Turn raw data into useful ML features, with reliable splits, transformations, and model comparisons.
Open course - ↗
Collaborative Filtering Recommender Systems
Learn how a product turns ratings, clicks, plays, or purchases into a short list of items for one person.
Open courseBefore thisClassical Machine Learning - ↗
Experimentation and Product Analytics
Turn an advertising-product experiment into defensible metrics, comparisons, and a product recommendation.
Open courseBefore thisProbability and Statistical Reasoning - ↗
Causal Inference
Learn what causal inference means, why assumptions matter, and how to estimate treatment effects step by step.
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Reinforcement Learning
Learn sequential decision-making from bandits and Q-learning through deep value methods, policy gradients, and offline reinforcement learning.
Open coursePut it into practiceProject · Perishable Inventory Replenishment - ↗
Machine Learning System Design
Work through ad selection, notification decisions, and search suggestions from one request to production.
Open course
AI engineering
Build reliable software around models: understand the model, retrieve evidence, use tools, evaluate behavior, and design the complete system.
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Software Engineering for AI Applications
Build the interfaces, modules, tests, release artifacts, and traces that make model-backed software maintainable.
Open coursePut it into practicePair programming · Debug Report Chunking Before LlamaIndex - ↗
Deep Learning Foundations
Learn the computation underneath transformers before attention, fine-tuning, and serving add more machinery.
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LLM Foundations
Understand what a language model actually computes, so the cost, latency, and failure modes of an LLM feature stop being surprises.
Open courseBefore thisDeep Learning FoundationsPut it into practicePair programming · Repair the LangChain RAG Tests - ↗
RAG and Retrieval Systems
Retrieve useful evidence, ground generated answers, and measure retrieval separately from generation.
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Agents and Tool Use
Learn the published mechanisms for reasoning, tool use, planning, reflection, memory, search, and multi-agent interaction.
Open coursePut it into practicePair programming · Complete the CrewAI Tool Guard Project · Repository Repair Agent - ↗
Evaluating and Monitoring LLM Applications
Measure LLM behavior across prompts, judges, RAG components, distribution shifts, guardrails, and long contexts.
Open coursePut it into practicePair programming · The Tuesday Prompt Tweak - ↗
Applied AI System Design
Design a complete AI product and defend its boundaries, capacity, evaluation, release plan, and failure behavior.
Open course - ↗
AI Security, Safety, and Privacy
Threat-model the complete AI system: its data, prompts, models, retrieval layer, tools, users, and logs.
Open coursePut it into practicePair programming · Complete the CrewAI Tool Guard
Model training and inference
Go deeper when the role expects training, alignment, serving internals, or multimodal systems.
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LLM Training, Data, and Scaling
Understand what pretraining consumes, how data quality changes the result, and why large-model training is a distributed-systems problem.
Open course - ↗
Post-training and Alignment
Learn how demonstrations, preference rankings, and carefully constrained updates turn a pretrained model into an assistant.
Open course - ↗
LLM Serving and Inference Economics
Find out where the latency and the money actually go, then change the number instead of guessing at it.
Open courseBefore thisLLM FoundationsPut it into practicePair programming · Repair the AI API Reliability Tests - ↗
Multimodal Models
Learn how published multimodal systems encode, align, connect, and evaluate images, audio, video, and language.
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