Agentic AI Learning Roadmap


🧠 *Step 1: Learn AI Basics*

Confused by terms like ML, DL, NLP? Start with a 1-hour YouTube tutorial that breaks it down with simple visuals. Perfect for beginners!


🐍 *Step 2: Learn Python*

Essential for building AI applications. Explore hands-on YouTube playlists + GitHub exercises. It's beginner-friendly!


💬 *Step 3: NLP Foundations*

Understand text processing:

- Regex, tokenization, stemming

- CountVectorizer & embeddings

📺 Free playlist available. Skip fastText/NLTK/Spacy if you want – focus on core concepts!


🤖 *Step 4: Generative AI Essentials*

Learn:

- LLMs 🦜

- Vector DBs: ChromaDB, Pinecone

- RAG (Retrieval Augmented Generation)

- LangChain + coding projects

🎥 3-hour YouTube course + 2 hands-on projects


🧪 *Step 5: More GenAI Projects*

Explore real-world applications using:

- Llama (Meta's open-source model)

- Hybrid models with regex, BERT, LLM

📺 Playlist for practical industry-relevant projects


🧭 *Step 6: Agentic AI Fundamentals*

Understand:

- What is agentic AI?

- No-code tools like N8N

- Model Context Protocol (MCP)

🎥 Quick explainer videos available


🔧 *Step 7: Hands-on Agentic AI*

Recommended frameworks:

- Agno

- LangGraph

- CrewAI (tutorial coming soon!)

📺 Practical tutorials on each, including LangGraph projects with stateful graphs, memory, human-in-the-loop, LangSmith, etc.


🌐 *Bonus: Build MCP Server*

Watch the video on how to set it up for a real use case


📚 *Deepen ML/DL Knowledge (Optional)*

If you're working at a lower-level:

- ML: Linear regression, decision trees, unsupervised learning

- DL: Neural networks, PyTorch, fine-tuning



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