🎓 AI & Machine Learning Hands-On Workshop

Welcome to the official repository for the 3-Day AI/ML Hands-On Workshop designed for 3rd-year Information Science Engineering students. This workshop emphasizes practical learning with real-world datasets and industry tools.

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📅 Workshop Overview

Duration: 3 Days
Focus: Applied Machine Learning, AI Applications, Chatbots, Emotion Detection, Deployment
Tools: Python, Scikit-learn, TensorFlow, OpenAI, FAISS, FastAPI, Google Colab

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🧠 Learning Objectives

By the end of the workshop, participants will be able to:

• Understand ML types: Supervised, Unsupervised, ANN
• Build real-world ML models and AI applications
• Create AI chatbots using vector databases and LLMs
• Deploy AI solutions using FastAPI and cloud platforms

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🗂️ Folder Structure

ai-ml-college-workshop/
├── day1_supervised_learning/
│   ├── 01_intro_to_ml.ipynb
│   ├── 02_house_price_regression.ipynb
│   ├── 03_spam_classifier.ipynb
│
├── day2_unsupervised_chatbot/
│   ├── 04_customer_segmentation.ipynb
│   ├── 05_market_basket_apriori.ipynb
│   ├── 06_chatbot_vector_llm.ipynb
│
├── day3_ann_deployment/
│   ├── 07_emotion_detector_tensorflow.ipynb
│   ├── 08_resume_screener.ipynb
│   ├── 09_ga_tsp_solver.ipynb
│   ├── 10_model_deployment_fastapi.ipynb

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📚 Key Topics Covered

• 📌 Introduction to AI & ML
• 📌 Supervised Learning: Classification, Regression
• 📌 Unsupervised Learning: Clustering, Association
• 📌 AI Chatbots with OpenAI & FAISS
• 📌 Emotion Detection with ANN (TensorFlow)
• 📌 Model Deployment with FastAPI

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⚙️ Requirements

• Python 3.8+
• Google Colab account or Jupyter Notebook
• API Key from OpenAI

Install additional dependencies:

pip install -r requirements.txt

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🧪 Datasets

Sample datasets are loaded via public URLs in each notebook.
For image datasets, you can upload your own or request the zipped folder if not provided here.

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🚀 Getting Started

1. Fork or clone this repository
2. Open notebooks in Google Colab or Jupyter
3. Run each cell with your own API keys (where required)
4. Try bonus challenges at the end of each notebook!

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🎯 Final Project Ideas

• AI Resume Screener
• Custom FAQ Chatbot
• Market Basket Recommendation System
• Emotion-based Music Recommender

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🙌 Acknowledgements

Special thanks to the students and faculty of [Your Institution] for participating!
Open-source libraries: Scikit-learn, FAISS, OpenAI, TensorFlow, FastAPI, mlxtend