Machine learning in Python: Classification
An introduction to machine learning in Python focusing on classification (supervised learning)
Frequency: Twice a year
Time: 2 x 3 hours
Language: English or Norwegian
Type of course: In person
Target audience:
UiO reseachers and students who want to get started with machine learning in Python.
A video (approximately 25 minutes) has been prepared that might be useful for those that are completely new to machine learning, with example use-cases in research.
Prerequisites:
Some familiary with Python is required (i.e. you can run python scripts from the REPL or an IDE). Basic knowledge of descriptive statistics and pandas is a plus.
Contents:
- Exploratory data analysis
- Binary classification
- Feature importance
- Multiclass classification
- Cross-validation
- Additional topics
- Preprocessing and pipelines
- Statistically comparing models
- Hyperparamater tuning
- Predicitng a continuous variable
Briefly about the course:
The focus will be on building and evaluating machine learning models in Python rather than an in-depth breakdown of specific algorithms using scikit-learn. We will be building models to distinguish between different categories of text based on linguistic features (including number of nouns, adjectives, etc.) using XGBoost.
Note: this is the equivalent of the R course using tidymodels
Contact
Send questions about the course to:
statistikk@usit.uio.no