Introduction to Machine Learning

Beginner-friendly notebooks and topic pages to learn ML hands-on.

Quickstart

1. Create a virtual environment

• macOS/Linux: python3 -m venv .venv && source .venv/bin/activate

2. Install basics: pip install -U jupyter numpy pandas seaborn scikit-learn matplotlib
3. Launch: jupyter notebook

How to use this repo

• Start with a topic page in docs/, then open the linked notebook in notebooks/.
• Data files are in data/; notebooks expect relative paths like data/Iris.csv.

Learning path (Beginner)

• Getting started: docs/getting-started.md
• Data preprocessing: docs/data-preprocessing.md → notebooks/data-cleaning.ipynb
• Classification (Iris): docs/classification.md → notebooks/iris-data-for-beginners.ipynb
• Next topics: docs/trees-and-ensembles.md, docs/unsupervised-learning.md, docs/regularization-and-overfitting.md, docs/regression.md

Repository structure

• docs/ — topic pages and guidance
• notebooks/ — hands-on notebooks
  • intro.ipynb
  • data-cleaning.ipynb
  • iris-data-for-beginners.ipynb
• data/ — small CSV datasets
  • Iris.csv
  • Iris for cleaning.csv
• exercises/ and solutions/ — practice (WIP)
• src/ — optional helpers (WIP)

Progress tracker

• Getting Started
• Data Preprocessing
• Classification
• Trees & Ensembles
• Unsupervised Learning
• Regularization & Overfitting
• Regression

Contributing

See CONTRIBUTING.md. Beginners welcome—docs updates, typo fixes, small notebooks are great first PRs.

License

MIT. See LICENSE.

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Legacy content from the previous long-form README is archived in docs/legacy/README-legacy.md.