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🧠 Machine Learning Faculty Development Program – Complete Repository Guide

Welcome to the Machine Learning Mini-Workshop Repository! This workshop is designed especially for MBA practitioner and non-technical learners who want a simple, practical, and visual introduction to Machine Learning.

This README will guide you from installation to running notebooks, and walk you through every folder in this repository.

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📌 1. Prerequisites

Before starting, ensure you have the following:

✔ A laptop

✔ Stable internet

✔ Basic familiarity with Excel-level data

✔ No programming experience required

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🧰 2. Install Required Tools

2.1 Install Git

Git helps you download and manage this project.

Windows

Download and install: https://git-scm.com/download/win

Mac

Git is pre-installed, but if needed: https://git-scm.com/download/mac

Linux

sudo apt install git

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2.2 Create a GitHub Account

You need this to access and store your projects.

Create your account: https://github.com/

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2.3 Install Python (3.10 or above)

Download from: https://www.python.org/downloads/

✔ During installation on Windows, check “Add Python to PATH”.

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2.4 Install Jupyter Notebook / JupyterLab

Open a terminal (CMD / PowerShell / Terminal) and run:

pip install jupyterlab notebook ipykernel

After installation, launch with:

jupyter lab

or

jupyter notebook

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📦 3. Download the Workshop Repository

In your terminal:

git clone https://github.com/<your-username>/<repository-name>.git
cd <repository-name>

(Replace with your GitHub repo link.)

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📥 4. Install All Required Python Libraries

Run this inside your repo folder:

pip install pandas numpy scikit-learn matplotlib seaborn \
requests beautifulsoup4 lxml opendatasets kaggle \
tensorflow torch notebook jupyterlab transformers \
sentence-transformers nltk spacy

This installs everything needed for:

✔ data preprocessing ✔ regression, classification, clustering models ✔ visualizations ✔ notebook execution

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🗂 5. Repository Structure

This workshop repository contains multiple folders. Each folder represents a complete part of the learning pipeline.

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📁 /01-Data-Discovery

This folder covers how to find datasets and research papers.

You will learn:

• How to download datasets (Kaggle, UCI, Google Dataset Search)
• How to structure research data
• Tools like Semantic Scholar, arXiv
• Spreadsheet basics

Material inside:

• data_sources.md
• Example dataset links
• Small sample CSV datasets

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📁 /02-Data-Quality

This contains the data cleaning workbook and explanations.

You will learn:

• Handling missing values
• Removing duplicates
• Encoding categories
• Scaling numeric data
• Train/test split

Files included:

• data_cleaning_workbook.ipynb
• sales_data.csv (demo dataset)
• Completed code solution
• Worksheets you can teach from

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📁 /03-Model-Selection

This folder introduces the 2 important Supervised ML families:

✔ Linear Regression

For predicting continuous values (e.g., sales, profit)

✔ Classification

For predicting labels (e.g., churn: yes/no)

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📘 Every model has:

• A ready-to-run Jupyter Notebook
• Step-by-step explanation
• Intuition with simple plots (scatter plots, clusters, decision boundaries)
• Visual explanations for MBA participants

Example files:

• linear_regression.ipynb
• classification.ipynb

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📁 /04-Training-Validation

This section focuses on how models learn.

Includes:

• Train/test split explanation
• Gradient descent intuition (MBA-friendly)
• Avoiding overfitting
• Hands-on training scripts

Files:

• training_basics.ipynb
• Visual plots for training curves

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📁 /05-Production-Readiness

This folder shows how to evaluate models properly.

You will learn:

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix
• Cross-validation

Files:

• validation_metrics.ipynb
• Interactive graphs with matplotlib & seaborn

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🚀 6. Running the Notebooks

Open your terminal inside the repo folder:

jupyter notebook

or

jupyter lab

Then click on any .ipynb file to open and run it.

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🎯 7. Learning Outcome

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

✔ Understand data cleaning

✔ Build simple regression, classification, clustering models

✔ Interpret model results

✔ Read plots and explain insights

✔ Work with Jupyter Notebook

✔ Apply ML thinking in business settings

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