Generative AI Roadmap for Freshers
A comprehensive 10-week learning plan to master Generative AI, LLMs, and AI content creation from scratch
This roadmap assumes 3-4 hours of daily study (2 hours learning + 1-2 hours practice)
Week 1-2: Python & AI Fundamentals
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 1: Python Basics for GenAI | ||||
| Day 1 |
Python Introduction - Installation & Setup - Jupyter Notebooks - Basic Syntax |
2 | 1 | Python Environments, Variables |
| Day 2 |
Data Structures - Lists, Tuples - Dictionaries, Sets - JSON Handling |
2 | 1.5 | Dictionary Operations, JSON Parsing |
| Day 3 |
APIs & Web Requests - REST APIs - HTTP Requests - JSON Handling |
2 | 2 | API Authentication |
| Day 4 |
NumPy & Pandas - Arrays & DataFrames - Data Manipulation - Data Cleaning |
2.5 | 2 | Data Preprocessing |
| Day 5 |
AI Introduction - What is Generative AI? - Applications & Use Cases - Ethics in GenAI |
2.5 | 1.5 | AI Ethics Principles |
| Day 6 |
Practice Day - API Integration Project - Data Processing |
1 | 3 | OpenAI API Basics |
| Day 7 |
Review Day - Week 1 Concepts - Q&A Session |
1 | 2 | Common API Errors |
| Week 2: Essential AI Concepts | ||||
| Day 8 |
Neural Networks Basics - Perceptrons - Activation Functions - Basic Architecture |
2.5 | 1.5 | Forward Propagation |
| Day 9 |
Deep Learning Intro - CNNs for Images - RNNs for Sequences - Transformers |
2.5 | 1.5 | Attention Mechanism |
| Day 10 |
NLP Fundamentals - Tokenization - Embeddings - Text Preprocessing |
2.5 | 1.5 | Word2Vec Basics |
| Day 11 |
Computer Vision Basics - Image Representation - Basic Image Processing - CV Applications |
2.5 | 1.5 | Pixel Manipulation |
| Day 12 |
Math for GenAI - Probability Basics - Statistics for AI - Linear Algebra Intro |
2 | 2 | Probability Distributions |
| Day 13 |
Practice Day - Text Processing Project - Basic Image Project |
1 | 3 | NLTK Basics |
| Day 14 |
Review Day - Week 2 Concepts - Q&A Session |
1 | 2 | Concept Integration |
Week 3-6: Large Language Models (LLMs)
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 3-4: LLM Fundamentals | ||||
| Day 15 |
LLM Introduction - What are LLMs? - GPT Architecture - Transformer Models |
2.5 | 2 | Transformer Architecture |
| Day 16 |
Prompt Engineering - Principles of Prompting - Effective Techniques - Few-shot Learning |
3 | 2 | Chain-of-Thought Prompting |
| Day 17 |
OpenAI API Deep Dive - Completions API - Chat Completions - Parameters Tuning |
3 | 2 | Temperature & Top-p |
| Day 18 |
LangChain Framework - Introduction to LangChain - Chains, Agents, Memory - Document Loaders |
2.5 | 2 | Agent Systems |
| Day 19 |
Vector Databases - Embeddings Storage - Similarity Search - Pinecone/ChromaDB |
2.5 | 2 | Semantic Search |
| Day 20 |
Practice Day - Build a Chatbot - Document Q&A System |
1 | 3 | RAG Architecture |
| Day 21 |
Review Day - Concepts Review - Q&A Session |
1 | 2 | API Best Practices |
| Week 5-6: Advanced LLM Applications | ||||
| Day 22 |
Fine-tuning LLMs - When to Fine-tune - Preparation of Data - Fine-tuning Process |
3 | 2 | Dataset Preparation |
| Day 23 |
Model Optimization - Quantization - Pruning - Distillation |
3 | 2 | Model Size Reduction |
| Day 24 |
AI Safety & Alignment - Bias Mitigation - Content Filtering - Ethical Considerations |
2.5 | 2 | Red Teaming |
| Day 25 |
Evaluation Metrics - Perplexity - BLEU Score - Human Evaluation |
2.5 | 2 | Quality Assessment |
| Day 26 |
Practice Day - Fine-tuning Project - Evaluation System |
1 | 3 | Hugging Face Transformers |
| Day 27-28 |
Review & Projects - LLM Concepts - Mini Projects |
1 | 4 | Project Deployment |
Week 7-10: Multimodal GenAI & Deployment
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 7-8: Image & Video Generation | ||||
| Day 29 |
Image Generation - Diffusion Models - DALL-E, Midjourney - Stable Diffusion |
3 | 2 | Prompt Crafting for Images |
| Day 30 |
Video Generation - Text-to-Video Models - Runway ML, Pika Labs - Animation Techniques |
3 | 2 | Temporal Consistency |
| Day 31 |
Audio Generation - Text-to-Speech - Music Generation - Voice Cloning |
3 | 2 | Voice Synthesis Ethics |
| Day 32 |
Multimodal AI - GPT-4 Vision - CLIP Model - Cross-modal Understanding |
3 | 2 | Vision-Language Models |
| Day 33 |
Practice Day - Image Generation Project - Multimodal Application |
1 | 3 | Stable Diffusion WebUI |
| Day 34 |
Review Day - GenAI Concepts - Q&A Session |
1 | 2 | Model Comparison |
| Week 9-10: Deployment & Real-world Applications | ||||
| Day 35-37 |
Cloud Deployment - AWS SageMaker - Google Vertex AI - Azure AI Services |
3 | 3 | Serverless Deployment |
| Day 38-40 |
API Development - FastAPI for GenAI - Streamlit Interfaces - Web Integration |
3 | 3 | API Rate Limiting |
| Day 41-44 |
Final Project - End-to-End GenAI System - Model Deployment - Performance Optimization |
2 | 4 | Cost Optimization |
| Day 45-50 |
Review & Career Prep - Core GenAI Concepts - Portfolio Development - Interview Preparation |
2 | 3 | Case Studies |
Key Recommendations
- Daily Practice: Experiment with different GenAI tools daily
- Projects: Build at least 5 complete GenAI projects by the end
- Community: Join GenAI communities like Hugging Face, OpenAI Discord
- Stay Updated: Follow latest research papers and model releases
- Ethics First: Always consider ethical implications of your GenAI applications