Where should I start?
You don’t need another list of links. You need an order.
The hardest part of learning AI isn’t finding content. It’s knowing what comes next.
Choose a destination. Follow the concepts in order. Each path distinguishes available reading from stages that are still planned.
AI Foundations ↗
Build a mental map before choosing a specialization.
- Statistics
- Python
- Machine Learning
- Model Evaluation
ML Engineer ↗
Move from a working model to a dependable service.
- Machine Learning
- Feature Engineering
- Model Evaluation
- APIs
NLP → LLM ↗
Follow the journey from words to contextual representations.
- Text Processing
- Embeddings
- Sequence Models
- Attention
Generative AI Engineer ↗
Design around the model, from retrieval to evaluation.
- Transformers
- LLMs
- Prompt Engineering
- Embeddings
Agentic AI Engineer ↗
Build systems that act with explicit boundaries.
- LLMs
- Tool Calling
- Structured Outputs
- RAG
Deep Learning Engineer ↗
Understand representations, gradients, and architectures.
- Linear Algebra
- Neural Networks
- Backpropagation
- CNN
A map, at your pace.
Stages show relative difficulty, not a promise of completion in a fixed number of hours. Revisit prerequisites whenever a concept needs more time. These are learning guides, not credentials or tracked courses.