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Machine learning in Python: Classification

An introduction to machine learning in Python focusing on classification (supervised learning)

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FrequencyTwice 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

Upcoming course

, Ole-Johan Dahls hus

Contact

Send questions about the course to:
statistikk@usit.uio.no