AI Engineer Roadmap 🤖

1. Python Foundations
• Learn: Syntax, loops, data structures, OOP, Git

2. Maths Statistics for AI
• Focus on: Linear algebra, probability, calculus, distributions

3. Machine Learning Algorithms
• Topics: Regression, classification, clustering, SVMs, model evaluation

4. Deep Learning Foundations
• Learn: Neural networks, CNNs, RNNs, regularization, optimizers

5. Natural Language Processing (NLP)
• Key Areas: Tokenization, embeddings, attention, sequence models

6. Transformers LLM Architectures
• Cover: Self-attention, encoder-decoder models, BERT, GPT, T5

7. Fine-Tuning Custom Model Training
• Techniques for: GPT, BERT, custom LLMs

8. LangChain Framework
• Build: LLM pipelines, tools, retrieval systems

9. LangGraph RAG Systems
• Concepts: Graph-based reasoning, orchestration, retrieval workflows

10. MCP Agentic AI Systems
• Create: Autonomous agents, multi-component systems, automation

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