Introduction to Machine learning in Python: Classification

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

Time and place:
The course consists of two sessions:

Tuesday 5th November, 12:15-15:00, in seminar room Prolog, Ole-Johan Dahls hus

Thursday 7th November, 12:15-15:00, in seminar room Postscript, Ole-Johan Dahls hus

Language: 
English

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
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Instructor:?
Luigi Maglanoc

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

Important: 
Participants must use their own PC or Mac (laptop) with Python (v >= 3.9).

Software requirements:

Download requirements.txt which contains all the required libraries. Install the libraries using the command below, preferably from within a virtual environment (e.g. conda, pyenv, poetry). Note: run the command from the directory/folder that contains requirements.txt

pip install -r requirements.txt

See this guide for a complete explanation on what is required, as well as suggested (optional) IDE setup (VS Code running Jupyter interactively) with conda virtual environment

Note: the python code can be run from any IDE (Spyder, Pycharm, etc).

 

Course material:

Click here for the whole course material (dataset, code, guide, requirements.txt)

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Published Aug. 22, 2024 11:39 AM - Last modified Aug. 22, 2024 11:39 AM