Learn the whole path from Python to AI engineering.
Use the learning map as your curriculum, then open Cortex Tutor to ask for explanations, examples, quizzes, assignments and study plans for any track.
Python Foundations
Syntax, functions, OOP, files, exceptions and practical problem solving.
SQL & Databases
Queries, joins, CTEs, windows, normalization, indexes and analytics.
Statistics for AI
Probability, distributions, sampling, confidence, testing and experiment thinking.
Data Science Workflow
EDA, preprocessing, feature engineering, visualization and reproducible analysis.
Machine Learning
Regression, classification, trees, ensembles, clustering, evaluation and tuning.
Deep Learning
Neural networks, optimization, CNN concepts, sequence models and transformers.
LLM Engineering
Embeddings, RAG, agents, prompting, evaluation, context and guardrails.
MLOps & Deployment
APIs, Docker, CI/CD, monitoring, drift, versioning and reliable serving.
Reinforcement Learning
MDPs, value functions, TD methods, policy gradients and applied RL.