This is study group for ml engineers to practice state of the art practices in machine learning.
- Description
- Need Help?
- Important References
- Code of Conduct
- Contributing
- Helpful Resources
- Technical Tools
This internship course amalgamates “Theory” and “Practice” – identifying that a machine learning scientist desires a survey of concepts combined with a strong application of practical techniques through labs. Primarily, the foundational material and tools of the Data Science practitioner are presented via Sk-Learn. Topics continue rapidly into exploratory data analysis and classical machine learning, where the data is organized, characterized, and manipulated. From week three, the students move from engineered models into custom application based approach.
Need help? Found an issue? Have a feature request? Checkout our support page
- Lab exercises: https://github.com/ELSPL/ml_practices_2018/tree/master/Labs
- Lab related material: https://github.com/ELSPL/ml_practices_2018/tree/master/Docs
- Wiki: https://github.com/ELSPL/ml_practices_2018/wiki
- Issue tracker: https://github.com/ELSPL/ml_practices_2018/issues
- Slack chat room: https://elsplworkspace.slack.com/messages/CF28SJ8PM/
We want everyone to feel welcome to contribute to our study group and participate in discussions. In that spirit please have a look at our code of conduct
We have provided an extensive guidelines for beginners and those contributing, which includes:
- Workflows for beginners
- Guidelines
- Issue Reporting
- Coding Standards, Style and Convention
- Pull Request Workflow with git and github
For more information, please read the see the contributing guide
- Git