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THIf I were to get started to learn GenAI — here’s exactly how I’d go about it 👇
(No degree, no prior experience needed. Just consistency and curiosity.)
Step 1: Learn Python (Weeks 1–4)
→ Do “Python for Everybody” or freeCodeCamp
→ Practice daily for 30 minutes
→ Push everything to GitHub — even your messy code
→ Start building your proof of work from Day 1
Step 2: Get ML Foundations (Weeks 5–8)
→ Take Andrew Ng’s ML course (still gold)
→ Pick 1 project on Kaggle (Titanic is great for starters)
→ Train a basic model → deploy it with Streamlit
→ Now you’ve built your first ML app 👏
Step 3: Deep Learning Phase (Months 3–4)
→ Learn through fast.ai — super hands-on
→ Fine-tune a small transformer (like BERT) on Hugging Face
→ Don’t aim for perfection, aim to finish one project well
Step 4: Understand Transformers & LLMs (Months 5–6)
→ Rebuild a mini GPT using Karpathy’s nanoGPT
→ This will help you actually understand self-attention, tokens, and training loops
→ Watch explainer videos + read blog posts to reinforce the concepts
Step 5: Learn the Real LLM Stack (Months 7–9)
→ LangChain for chaining prompts & calling tools
→ LangGraph for multi-agent workflows and memory
→ Weaviate or LanceDB for retrieval (RAG setups)
→ QLoRA for fine-tuning open models
→ vLLM for efficient inference
Step 6: Build and Publish (Months 10–12)
→ Choose one simple use case (like a research assistant or AI chatbot)
→ Build it end-to-end using the stack you’ve learned
→ Make a demo video, write a short architecture breakdown
→ Share on GitHub, LinkedIn, and Twitter — this is your new resume
Portfolio project ideas: https://www.instagram.com/p/DG6l_MrShZ8/?igsh=NTc4MTIwNjQ2YQ==
( Link also in "Learn AI" highlights)
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