AI Learning Roadmaps
Structured paths to master different areas of Artificial Intelligence
Beginner to ML Engineer
Beginner
6-12 months
- Math Foundations (Linear Algebra, Statistics)
- Python Programming & Data Manipulation
- Machine Learning Basics
- Supervised Learning Algorithms
- Model Evaluation & Validation
- Deep Learning Fundamentals
- MLOps & Deployment
LLM Specialist Path
Intermediate
4-8 months
- NLP Fundamentals
- Transformer Architecture
- Pre-trained Models (BERT, GPT)
- Fine-tuning Techniques
- Prompt Engineering
- LLM Applications & RAG
- LLM Evaluation & Safety
AI Agent Developer
Advanced
3-6 months
- LLM Foundations
- Tool Use & Function Calling
- Agent Frameworks (LangChain, etc.)
- Multi-Agent Systems
- Agent Planning & Reasoning
- Production Agent Systems
- Agent Safety & Alignment
Foundations
Prerequisites
Mathematical Foundations
- - Linear Algebra (vectors, matrices, eigenvalues)
- - Calculus (derivatives, gradients)
- - Statistics & Probability
- - Discrete Mathematics
Programming Skills
- - Python programming
- - Data structures & algorithms
- - Version control (Git)
- - Command line basics
Recommended Resources
Online Courses
- - Andrew Ng's Machine Learning Course
- - Fast.ai Practical Deep Learning
- - DeepLearning.AI Specializations
Books & Publications
- - "Hands-On Machine Learning" by Aurélien Géron
- - "Deep Learning with Python" by François Chollet
- - Papers from arXiv, NeurIPS, ICML