---

🧠 AI Technical Training

Glossary - https://decoding-data-science.github.io/aiglossary2025/#resources

Welcome to your AI Technical Training Repository! This repo contains all the notebooks, datasets, and resources you’ll use during the first 5 days of training.

We’ll be using Google Colab for everything — no software installation required.

Kahoot https://github.com/Decoding-Data-Science/nov25/tree/main/kahoot Slides https://github.com/Decoding-Data-Science/nov25/tree/main/Slides

🚀 Quick Start

1. Open Google Colab: 👉 https://colab.research.google.com

2. Open a Notebook: Click one of the links below to launch directly in Colab.

---

📅 Training Schedule & Notebooks

Day	Topic	Open in Colab
Day 1	Python & AI Foundations
Day 2	Exploratory Data Analysis
Day 3	Machine Learning Models
Day 4	Deep Learning Intro
Day 5	MLOps & Deployment Basics

Replace <your-org>/<your-repo> with your actual GitHub path (e.g., decodingdatascience/ai-training).

---

📂 Folder Structure

ai-training/
│
├── data/             ←  sample datasets
├── notebooks/        ←  Day 1 – Day 5 notebooks
├── resources/        ←  slides + cheat sheets
└── README.md

---

🧩 How to Use the Notebooks

• Run cells top → bottom
• Practice sections are marked with # Your Turn!
• Save your own copy: File → Save a copy in Drive

---

💡 Helpful Tips

• If an error appears → Runtime → Restart and run all
• Keep your internet connection active for Colab
• Try to experiment with every example — learning by doing!

---

🤝 Contribute / Improve

1. Fork the repo
2. Edit a notebook or add a resource
3. Submit a pull request

All contributions welcome — even typo fixes and better examples!

---

AI Technical Training (Nov 2025) — Trainer Cohort Repository

This repository contains all training assets for the Nov 2025 cohort:

• Google Colab notebooks (Python → ML → DL → GenAI/RAG/Agents)
• Datasets under /data
• Slides under /Slides
• Kahoot quizzes under /kahoot
• Supporting PDFs and reference materials

Glossary / Resources: https://decoding-data-science.github.io/aiglossary2025/#resources

---

Quick Start (Trainers)

1. Use Google Colab for delivery: https://colab.research.google.com
2. Ask learners to open the notebook, then: Runtime → Run all (or run top-to-bottom)
3. If anything breaks: Runtime → Restart runtime → Run all

Repo shortcuts

• Slides: https://github.com/Decoding-Data-Science/nov25/tree/main/Slides
• Kahoot: https://github.com/Decoding-Data-Science/nov25/tree/main/kahoot
• Data: https://github.com/Decoding-Data-Science/nov25/tree/main/data

---

Core Training Schedule (Suggested: Days 0–5)

Use this as the default classroom flow. You can swap notebooks based on cohort pace.

Day	Topic	Primary Notebook
Day 0	Python Foundations	Open in Colab
Day 0	Stats Foundations	Open in Colab
Day 1	Exploratory Data Analysis (Power Plant)	Open in Colab
Day 2	Regression (baseline ML)	Open in Colab
Day 3	Classification (Logistic Regression)	Open in Colab
Day 4	Trees + Unsupervised Learning	Decision Tree · Unsupervised
Day 5	Deep Learning Intro	Open in Colab

Alternative / dated versions (use only if needed)

• 18 Nov Stats: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/18_nov_Day0_Stats.ipynb
• 19 Nov Unsupervised: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/19_nov_unsupervised_learning.ipynb
• 20 Nov Logistic Regression: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/20_nov_logistic_regression1.ipynb

---

GenAI / RAG / Agentic Modules (Add-on Track)

LLM + API Wrappers (Weather demo)

• Weather API demo: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/weather_api_nov2025.ipynb
• Weather LLM wrapper: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/Weather_LLM_wrapper.ipynb
• Weather (updated variant): https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/new_20_nov_weather_api_nov2025.ipynb

RAG (Vector Stores / Pinecone)

• First RAG: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/your_firstRAG_enec.ipynb
• Vector stores + Pinecone index demo: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/ENEC_rag_vector_stores_pineconeindexdemo.ipynb

Agents (LangChain / CrewAI) + Industrial Support Ticketing

• LangChain chains + agent: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/Langchain_chains_agent_15thov.ipynb
• CrewAI multi-agent (Level 1): https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/crewai_multiagent_level_1.ipynb
• ENEC ticketing (CrewAI scenario): https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/ENEC_ticket_crewai_new.ipynb

Evaluation (LLM / RAG recipes)

• Evaluation recipe: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/1_evaluation_recipe.ipynb

---

Projects & Demos (Optional)

• Power plant forecasting: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/Predicting_Power_Plant_Output_Using_Weather_Data.ipynb
• Business questions (power plant): https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/business_quesitons_powerplant.ipynb
• First Python chatbot: https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/first_python_chatbot.ipynb
• Visual AI (clip project): https://colab.research.google.com/github/Decoding-Data-Science/nov25/blob/main/energy_plant_visual_ai_clip_project.ipynb

---

Folder Structure

nov25/
├── Slides/                      # slide decks used in sessions
├── kahoot/                      # Kahoot quizzes + exports
├── data/                        # datasets used by notebooks
├── generativeai/                # GenAI assets (if applicable)
├── *.ipynb                      # notebooks (core + add-ons)
├── *.pdf                        # reference PDFs / proposals
└── README.md


## 📞 Need Help?

If you’re stuck:

* Ask in the **training WhatsApp group** 📱
* Or tag your mentor/instructor in Colab comments