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ml-oneday-course

This is a one-day machine learning introductory course for beginners

https://github.com/gozsari/ml-oneday-course

Science Score: 67.0%

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    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (12.4%) to scientific vocabulary

Keywords

anomaly-detection classification clustering course dimensionality-reduction machine-learning machine-learning-algorithms ml-course ml-workflow regression scikit-learn supervised-learning unsupervised-learning
Last synced: 7 months ago · JSON representation ·

Repository

This is a one-day machine learning introductory course for beginners

Basic Info
  • Host: GitHub
  • Owner: gozsari
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 3.68 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 5
  • Open Issues: 0
  • Releases: 1
Topics
anomaly-detection classification clustering course dimensionality-reduction machine-learning machine-learning-algorithms ml-course ml-workflow regression scikit-learn supervised-learning unsupervised-learning
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Introduction to Machine Learning: One-Day Course

DOI GitHub repo size GitHub contributors GitHub issues GitHub pull requests Course Machine Learning Python Jupyter Open Science GitHub Codespaces MIT License
GitHub stars GitHub forks

A beginner-friendly one-day Machine Learning (ML) course covering fundamental concepts with hands-on examples.

📌 Overview

This course introduces the basics of Supervised & Unsupervised Learning using Python and Scikit-learn.
You'll explore Regression, Classification, Clustering, Dimensionality Reduction, and Anomaly Detection through interactive Jupyter Notebooks.

📄 Slides: Presentation
📂 Notebooks: Course Materials
📘 Detailed Course Content: COURSE_CONTENT.md

This course has been prepared as part of the course "Introduction to Digital Resources" conducted by Chalmers e-Commons.

Machine Learning

Image generated by AI

Quickstart: Run on Codespaces or Locally

You can run the course notebooks on GitHub Codespaces or locally on your machine.

Run on GitHub Codespaces

Click Code > Open with Codespaces and start immediately!

Run Locally

1️⃣ Clone the repository:
sh git clone https://github.com/gozsari/ML-OneDay-Course.git cd ML-OneDay-Course 2️⃣ Create a virtual environment:
sh python3 -m venv .venv source .venv/bin/activate 3️⃣ Install dependencies:
sh pip install -r requirements.txt 4️⃣ Run Jupyter Notebook:
sh jupyter notebook 5️⃣ Open the Jupyter Notebook in your browser and start learning!

📦 Dependencies

| Package | Version |
|----------|----------|
| Python | 3.11+ |
| NumPy | latest |
| Pandas | latest |
| Scikit-learn | latest |
| Matplotlib | latest | | Seaborn | latest | | Jupyter | latest | | joblib | latest |

🔖 Citation

If you use this course, please cite it using the information in CITATION.cff.

📜 License

This project is licensed under the MIT License.

Acknowledgements

Special thanks to Leon Boschman for contributing ideas, slides, and feedback.

Owner

  • Name: GOKHAN OZSARI
  • Login: gozsari
  • Kind: user
  • Location: Ankara, TURKEY
  • Company: MIDDLE EAST TECHNICAL UNIVERSITY

Ph.D. Candidate, Research/Teaching Assistant, at CEng, METU

Citation (CITATION.cff) 

cff-version: 1.2.0
title: "Introduction to Machine Learning Course"
authors:
  - family-names: "Özsari"
    given-names: "Gökhan"
    affiliation: "Chalmers University of Technology"
    orcid: "https://orcid.org/0000-0002-3023-9843"
abstract: |
  This repository contains materials for the "Introduction to Machine Learning" course, 
  including presentations, Jupyter notebooks, and in-class assignments. The course 
  covers supervised and unsupervised learning, machine learning workflows, and practical 
  hands-on projects.
keywords:
  - machine learning
  - supervised learning
  - unsupervised learning
  - clustering
  - classification
  - regression
  - Python
  - scikit-learn
license: "MIT"
repository-code: "https://github.com/gozsari/ML-OneDay-Course" 
doi: "10.5281/zenodo.14249784" 
date-released: 2024-11-29
version: "1.0.0"

# Citation examples
preferred-citation:
  type: software
  title: "Introduction to Machine Learning Course Repository"
  authors:
    - family-names: "Özsari"
      given-names: "Gökhan"
  version: "1.0.0"
  url: "https://github.com/gozsari/ML-OneDay-Course"
  date-released: 2024-11-30

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Dependencies

.devcontainer/Dockerfile docker
  • mcr.microsoft.com/devcontainers/python 3.11 build
.devcontainer/requirements.txt pypi
  • joblib * development
  • matplotlib * development
  • notebook * development
  • numpy * development
  • pandas * development
  • scikit-learn * development
  • seaborn * development