Generative AI Roadmap 2025 – Complete Beginner to Advanced GenAI Tutorials

Learn Generative AI step by step with this complete roadmap. Covers Python, ML, NLP, Transformers, LLMs, prompt engineering, RAG, tools, projects, and deployment for beginners to advanced learners.

Phase 1: Programming & AI Foundations (0–4 Weeks)

What to Learn

  1. Python fundamentals
  2. Variables, loops, functions
  3. Object-oriented programming
  4. Working with APIs
  5. Data handling
  6. NumPy
  7. Pandas
  8. Math for AI
  9. Linear Algebra (vectors, matrices)
  10. Probability & Statistics
  11. Basic Calculus
  12. Git & GitHub basics

Outcome

  1. Comfortable with Python coding
  2. Understand how data and math power AI models


Phase 2: Machine Learning Basics (4–8 Weeks)

What to Learn

  1. What is Machine Learning
  2. Types of ML
  3. Supervised Learning
  4. Unsupervised Learning
  5. Reinforcement Learning
  6. Core concepts
  7. Training vs testing
  8. Overfitting & underfitting
  9. Bias & variance
  10. Algorithms
  11. Linear Regression
  12. Logistic Regression
  13. Decision Trees
  14. Random Forest
  15. K-Means

Tools

  1. Scikit-Learn
  2. Jupyter Notebook


Phase 3: Deep Learning & Neural Networks (6–10 Weeks)

What to Learn

  1. Artificial Neural Networks
  2. Activation functions
  3. Loss functions
  4. Optimizers (SGD, Adam)
  5. Backpropagation
  6. CNN (for images)
  7. RNN & LSTM (for sequences)

Frameworks

  1. TensorFlow
  2. PyTorch


Phase 4: NLP & Transformer Models (6–10 Weeks)

What to Learn

  1. Natural Language Processing basics
  2. Tokenization
  3. Stemming & Lemmatization
  4. Word embeddings
  5. Word2Vec
  6. GloVe
  7. Attention mechanism
  8. Transformer architecture
  9. BERT, GPT overview

Outcome

  1. Understand how language models work internally


Phase 5: Generative AI Core Concepts (8–12 Weeks)

What to Learn

  1. What is Generative AI
  2. Types of Generative Models
  3. Large Language Models (LLMs)
  4. GANs
  5. VAEs
  6. Diffusion Models
  7. Prompt Engineering
  8. Zero-shot prompts
  9. Few-shot prompts
  10. Chain-of-Thought

Models to Know

  1. GPT
  2. LLaMA
  3. Claude
  4. Gemini


Phase 6: GenAI Tools & Frameworks (Ongoing)

Tools to Learn

  1. Hugging Face Transformers
  2. LangChain
  3. LlamaIndex
  4. OpenAI API
  5. Vector Databases
  6. FAISS
  7. Pinecone
  8. ChromaDB

Concepts

  1. Embeddings
  2. Retrieval-Augmented Generation (RAG)


Phase 7: Projects & Real-World Applications

Project Ideas

  1. AI chatbot using LLMs
  2. Document question-answer system (RAG)
  3. Resume or content generator
  4. Code assistant
  5. Image generation app
  6. AI agent for automation

Deployment

  1. FastAPI
  2. Streamlit
  3. Docker
  4. Cloud deployment (AWS / Azure / GCP)


Phase 8: Advanced & Production-Level GenAI

What to Learn

  1. Fine-tuning LLMs
  2. Prompt optimization
  3. Reducing hallucinations
  4. AI safety & ethics
  5. Multi-modal AI (text + image + audio)
  6. AI agents & workflows