Master Generative AI — from Python to Agents, RAG, Multimodal AI, and Robotics
Learn. Build. Specialize. Deploy. Repeat.
Generative AI is transforming the way we think, learn, and build — from code generation and AI art to research, tutoring systems, co-pilots, and autonomous agents. But most learners face the same challenge: where do I start, and how do I go deep?
This roadmap is your structured, curated, and up-to-date path — built with real-world tools, hands-on projects, and a deep focus on how to apply GenAI across industries and specializations.
| Phase | What You’ll Learn | Start Learning |
|---|---|---|
| 1️⃣ Python & Tools | Programming fundamentals, Git, Jupyter, virtual environments, data libraries | 📂 Phase 1 |
| 2️⃣ Machine Learning | EDA, supervised/unsupervised learning, model evaluation, stats | 📂 Phase 2 |
| 3️⃣ MLOps & Deployment | ML pipelines, CI/CD, tracking, model deployment, Docker | 📂 Phase 3 |
| 4️⃣ Deep Learning | Neural nets, CNNs, RNNs, transformers, training best practices | 📂 Phase 4 |
| 5️⃣ Core GenAI Techniques | Prompt engineering, GPTs, diffusion, evaluation, multimodal AI | 📂 Phase 5 |
| 6️⃣ Advanced GenAI Systems | LoRA/QLoRA fine-tuning, RAG, Agents (AutoGPT, BabyAGI), safety & ethics | 📂 Phase 6 |
| 7️⃣ Specialization & Capstone | Domain-driven apps (health, finance, education), full-stack GenAI systems | 📂 Phase 7 |
| 8️⃣ RL, Vision-Language & Robotics | Reinforcement learning, multimodal AI, embodied agents & robotics | 📂 Phase 8 |
Throughout this roadmap, you’ll work on several guided projects and a final capstone, such as:
- 🧑💬 Prompt-powered chat assistants
- 📚 Document-based Q&A with RAG pipelines
- 🛠️ Fine-tuned LLMs for custom use-cases
- 🌐 Streamlit or Gradio GenAI apps with real-time APIs
- 🤖 Intelligent Agents using AutoGPT/BabyAGI
- 🧠 Simulated robots with RL-based navigation and instruction following
- 🧪 Capstone: A domain-specific GenAI application you can deploy and share
| Category | Tools & Libraries |
|---|---|
| Core Development | Python, Pandas, NumPy, Jupyter, Git |
| ML & DL | Scikit-learn, PyTorch, TensorFlow, Keras |
| Generative AI | Hugging Face Transformers, Diffusers, PEFT, OpenAI APIs |
| MLOps | MLflow, Weights & Biases, Docker, GitHub Actions |
| Deployment | Streamlit, Gradio, FastAPI, Hugging Face Spaces |
| Retrieval & Agents | LangChain, LlamaIndex, Pinecone, ChromaDB, AutoGPT |
| RL & Robotics | OpenAI Gym, RLlib, Unity ML-Agents, ROS, Habitat AI |
| Multimodal | CLIP, BLIP, GPT-4V, Gemini API |
At the end of the roadmap, you’ll create a complete GenAI system:
- 🔍 Choose your domain (e.g., healthcare, finance, creativity, robotics)
- ⚙️ Combine RAG, prompting, or fine-tuning
- 🧱 Build a web app (Streamlit, Gradio) or full-stack deployment
- 🧠 Add agent memory, planning, or tool use
- 🚀 Share your app, blog, or walkthrough online
- 🧑🎓 Students building portfolio-worthy AI capstones
- 👩💻 Devs pivoting into LLMs, GenAI apps, or ML engineering
- 🧠 Researchers replicating papers or building agents
- 🏢 Professionals deploying AI into real domains and industries
This is more than a roadmap — it’s a builder’s toolkit, a research springboard, and a career-launcher.
The future belongs to those who can build with AI, not just consume it.
Let’s learn deeply. Create responsibly. Share generously.