Topics
7 domains, 37 topics — distilled from curated books. Pick a domain to start.
AI/ML Foundations
The vocabulary and mental models behind machine learning: data, labels, loss, optimization, generalization, and evaluation.
6 topicsClassical Machine Learning
The practical non-deep-learning toolbox: linear models, trees, ensembles, clustering, feature engineering, and validation.
5 topicsDeep Learning
Neural networks as representation learners: layers, backpropagation, optimizers, regularization, embeddings, and modern architectures.
5 topicsLLMs & Generative AI
How large language models work and how to reason about tokens, context, pretraining, prompting, RAG, tools, and agents.
6 topicsApplied AI Engineering
The developer-facing craft of shipping AI features: structured outputs, retrieval, evaluation, latency, cost, reliability, and observability.
5 topicsProduction ML & MLOps
The systems side of ML: pipelines, serving, online inference, training-serving skew, monitoring, experiments, and rollout safety.
5 topicsResponsible AI
Bias, privacy, explainability, prompt injection, provenance, and human review treated as engineering constraints, not side quests.
5 topics