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100 Days of GenAI Challenge — Daily Tasks
Days 1–10: Python + Conda Setup
Day 1: Install Miniconda/Anaconda, Python 3.11
Day 2: Create your Conda environment and activate it
Day 3: Install Jupyter Notebook, create your first Notebook
Day 4: Python Basics: Variables, Data Types, Input/Output
Day 5: Python Basics: Functions, Conditionals, Loops
Day 6: Python Basics: Lists, Dictionaries, Tuples, Sets
Day 7: Python Basics: Classes and Object-Oriented Programming
Day 8: Setup Git + GitHub, create your first repository
Day 9: Write a README.md with Markdown for your first repo
Day 10: Share your setup journey on LinkedIn
Days 11–20: Core Machine Learning Basics
Day 11: Install pandas, numpy, matplotlib
Day 12: Load and clean a dataset (CSV) using pandas
Day 13: Build a simple Linear Regression model (e.g., house prices)
Day 14: Visualize data (scatter plots, histograms)
Day 15: Train/Test Split - Understand overfitting/underfitting
Day 16: Classification: Train a Decision Tree classifier
Day 17: Evaluate models: Accuracy, Confusion Matrix
Day 18: Build a simple Machine Learning Pipeline
Day 19: Save model using joblib
Day 20: Share your first ML project on LinkedIn
Days 21–30: Language and LLM Fundamentals
Day 21: Introduction to NLP (Text preprocessing)
Day 22: Install and explore Huggingface Datasets
Day 23: Tokenization basics (word-level and subword)
Day 24: Install and use OpenAI API for simple prompts
Day 25: Understand Prompt Parameters: temperature, top_p, max_tokens
Day 26: Few-shot Prompting: design your own prompt examples
Day 27: Chain of Thought Prompting — Reasoning examples
Day 28: Install and load GPT2 using Huggingface Transformers
Day 29: Compare GPT2 generated text vs OpenAI API text
Day 30: Write a blog about "Prompting My First Language Models"
Days 31–40: Embeddings and RAG (Retrieval-Augmented Generation)
Day 31: What are Embeddings? Install OpenAI Embeddings
Day 32: Generate sentence embeddings and visualize with PCA
Day 33: Install FAISS, index your embeddings locally
Day 34: Perform semantic search on your FAISS index
Day 35: Load a PDF document, split into chunks
Day 36: Generate embeddings for document chunks
Day 37: Create a basic RAG system (Search + LLM Answer)
Day 38: Try Huggingface Embeddings instead of OpenAI
Day 39: Experiment with Chroma or Qdrant as vector store
Day 40: Publish a post: "I Built My First RAG Bot!"
Days 41–50: LangChain Basics
Day 41: Install LangChain
Day 42: Build your first simple LangChain LLM Chain
Day 43: Create a Sequential Chain (multi-step task)
Day 44: Add Memory to your chain (ConversationBufferMemory)
Day 45: Build a Question-Answering Chain using documents
Day 46: Create your own Custom Prompt Templates
Day 47: Create your first Tool and use in LangChain Agent
Day 48: Chain Tools into a mini-Agent
Day 49: Integrate LangChain Agent + FAISS for smart search
Day 50: Share your Agent project on LinkedIn
Days 51–60: Tool-Using Agents
Day 51: Introduction to Tools and Agents (high-level)
Day 52: Add Search API as a Tool
Day 53: Add Calculator API as a Tool
Day 54: Create a multi-tool agent that decides what to use
Day 55: Understand "tool selection" based on queries
Day 56: Write custom Output Parsers for LangChain
Day 57: Create a Financial Analyst Agent (search + calculate)
Day 58: Create a simple Email Generator Agent
Day 59: Implement Logging and Error Handling in your Agents
Day 60: Publish: "How I Built a Multi-Tool AI Agent"
Days 61–70: Autonomous Agents (AutoGPT/BabyAGI Inspired)
Day 61: Study how BabyAGI works (repo exploration)
Day 62: Build a simple Task Creation -> Execution loop
Day 63: Add memory store to keep track of completed tasks
Day 64: Add prioritization logic
Day 65: Self-evaluate task quality
Day 66: Replan based on new inputs (dynamic replanning)
Day 67: Create a "Daily Content Generator" autonomous agent
Day 68: Limit API usage to optimize costs
Day 69: Debugging Autonomous Flows
Day 70: Share your experience: "My First Autonomous Agent"
Days 71–80: FastAPI + Docker for GenAI Apps
Day 71: Install and learn FastAPI Basics
Day 72: Build a simple FastAPI endpoint for OpenAI API
Day 73: Serve your RAG app as a FastAPI service
Day 74: Docker Basics: Containers, Images, DockerHub
Day 75: Write a Dockerfile for your FastAPI app
Day 76: Build and run your container locally
Day 77: Push your app to DockerHub
Day 78: Deploy your container to a free cloud (Render, Fly.io)
Day 79: Monitor API logs and handle exceptions gracefully
Day 80: Publish: "Deploying My First GenAI API to Cloud"
Days 81–90: Prefect Orchestration for GenAI Workflows
Day 81: Prefect Basics: Flows and Tasks
Day 82: Create a Prefect flow for document processing
Day 83: Schedule your flow with Prefect Work Pools
Day 84: Build a Flow that runs every day: "Daily Tweet Writer"
Day 85: Add concurrency and retries to your Prefect flows
Day 86: Use environment variables securely (Prefect Blocks)
Day 87: Automate RAG Reindexing weekly
Day 88: Handle Prefect Failures with Notifications (Slack, Email)
Day 89: Prefect Deployment using Prefect YAML
Day 90: LinkedIn Post: "Automating GenAI Workflows using Prefect"
Days 91–100: Agentic Workflows, MCP, and A2A
Day 91: Introduction to MCP (Model Context Protocol)
Day 92: Understand StdioTransport, Server classes
Day 93: Install and run MCP Python SDK
Day 94: Build a simple MCP Server with one Tool
Day 95: Extend to Multi-Tool MCP Server
Day 96: Build an Agent that reads documentation and writes summary
Day 97: Build another Agent that reviews summaries and corrects them
Day 98: Connect Agents in A2A (Agent to Agent) style
Day 99: Showcase: Create a multi-agent autonomous publishing system
Day 100: Grand Finale Post:
"From Python Basics to Autonomous Agents: My 100 Days of GenAI Journey"
Extra:
Every 7–10 days: Do a LinkedIn Post summarizing your progress.
GitHub Public Portfolio: push code every week.
Use consistent hashtags: #100DaysOfGenAI | #LearnInPublic | #AIStudentChallenge.
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