machine-learning-workshop

This repository contains materials and notebooks for a practical Machine Learning workshop.
It is structured as a step-by-step introduction to core ML concepts, progressing from data handling to supervised and neural network models.

---

Workshop Structure

The workshop is organized into the following modules:

1. Data Handling

• Data loading and preprocessing
• Cleaning and transformation
• Feature engineering basics

Notebook:

• 01_data_handling.ipynb

---

2. Unsupervised Learning

• Clustering concepts
• Dimensionality reduction (if included)
• Exploring structure in unlabeled data

Notebook:

• 02_unsupervised.ipynb

---

3. Supervised Learning

• Classification and regression
• Model training and evaluation
• Performance metrics

Notebook:

• 03_supervised.ipynb

---

4. Neural Networks (Supervised)

• Introduction to neural networks
• Training simple feed-forward models
• Model evaluation and tuning

Notebook:

• 04_supervised_nn.ipynb

---

Repository Structure

• data/ – Workshop datasets
• notebooks/ – Additional notebook materials
• Root notebooks – Main workshop modules

---

Requirements

• Python 3.x
• Jupyter Notebook or Google Colab
• Common ML libraries:
  • NumPy
  • Pandas
  • Matplotlib / Seaborn
  • Scikit-learn
  • (Optional) TensorFlow or PyTorch for neural networks

Install dependencies if needed:

pip install numpy pandas matplotlib seaborn scikit-learn

---

Usage

Run locally:

jupyter notebook

Or open the notebooks directly in Google Colab.

Follow the notebooks in order (01 → 04) for a structured learning path.

---

Target Audience

• Beginners in Machine Learning
• Students attending an ML workshop
• Developers transitioning into AI/ML
• Anyone wanting a practical introduction to ML

---