Learning Paths

Structured curricula to become an AI engineering expert

Career Track

🎯All Paths 🤖LLM Engineer ⚙️MLOps 🔬Researcher 🚀Full-Stack 📊Data Science

Level

All Levels Beginner Intermediate Advanced

🤖LLM EngineerIntermediate

🤖

LLM Engineer Path

Master production-ready LLM application development. From transformer fundamentals to deploying RAG systems, fine-tuning, and AI agents. Based on industry best practices from OpenAI, Anthropic, and leading AI labs.

16 weeks

12 milestones

Transformer ArchitecturePrompt EngineeringRAG SystemsFine-tuning (LoRA/QLoRA)+5

⚙️MLOps EngineerIntermediate

⚙️

MLOps Engineer Path

Learn to build, deploy, and operate machine learning systems at scale. Master the complete ML lifecycle from experiment tracking to production monitoring. Based on practices from top ML teams at Google, Meta, and Netflix.

14 weeks

10 milestones

Experiment TrackingModel RegistryML PipelinesModel Serving+6

📊Data ScientistIntermediate

📊

Data Scientist Path

Master the complete data science toolkit from statistics to machine learning to communication. Learn to extract insights from data and drive business decisions. Covers the essential 95% of what data scientists do daily.

16 weeks

11 milestones

Python for Data ScienceSQL MasteryStatistics & ProbabilityMachine Learning+4

🚀Full-Stack AIIntermediate

🚀

Full-Stack AI Engineer Path

Become a versatile AI engineer who can build end-to-end AI-powered applications. From frontend to backend to ML models - own the entire stack. Perfect for startups and product teams.

18 weeks

9 milestones

React/Next.jsPython BackendLLM IntegrationRAG Systems+4