04-27, 12:00–12:45 (Europe/Vienna), i2
GitHub Repository, thank you for coming!
Or rather: How to get started with Machine Learning using Python?
We apply scikit-learn to develop a simple Machine Learning model.
What is this talk about?
For a specific (and simple) use case, we will go through the steps needed to learn from data by training and evaluating a Machine Learning model.
In doing so, we will touch on:
- Problem setting and terminology
- Learning and predicting
- Feature selection and importance, Hyperparameters, Cross Validation
What is this talk NOT about?
- Deep Learning
- Different Machine Learning models
- In-depth scikit-learn ...
- AI and PowerPoint ;)
As an introductory level talk, no prerequisites are required. Familiarity with Python syntax may be advantageous but is not mandatory.
Claus is a Data Scientist and event organizer passionate about structuring things and learning from data. Over the past years, he has worked in areas related to applied and industrial research, algorithmic trading and social media.
Besides statistics and Machine Learning, he is interested in software craftsmanship, communities and open source.
Motto: “Code or it didn‘t happen!”
In addition to his employment, he works as freelance consultant and trainer for (scientific) Python and data analysis.