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)
Goal
This roadmap sequences topics so each day builds on the last—skip ahead only after exercises feel easy.
Method
Block time for practice: reading without coding rarely sticks for technical skills.
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
Comprehensive Generative AI Learning Path
This Generative AI roadmap on Nikhil Learn Hub provides a structured learning path: Explore generative AI concepts, LLMs, prompt engineering, transformers, and practical AI application development.
Use the schedule, weekly tables, and practice notes on this page to pace your progress. Keep the Generative AI cheatsheet open for syntax and API reminders during exercises.
Foundation phase
- Core concepts and terminology for this stack
- Guided exercises and small coding drills
- Hands-on labs aligned with each milestone
- Review checkpoints before moving forward
Advanced phase
- Multi-topic projects and integration tasks
- Performance, security, or scalability basics
- Tooling and workflow patterns used in industry
- Interview, certification, or portfolio preparation
Who Should Follow This Roadmap
Students, career switchers, and developers upskilling in Generative AI can follow this roadmap for credible study order instead of scattered tutorials.
Related Resources on Nikhil Learn Hub
- Generative AI cheatsheetquick reference while you follow this roadmap
- Technology roadmaps hubbrowse all structured learning paths
- Technology hubbroader programming and AI resources