EHML

Machine Learning course at the Business School of the Bern University of Applied Sciences

Usage

You can run all the notebooks either via the binder service or via Google Colab using the badges at the top of this file.

In order to run the code locally, we recommend to use conda to create an environment with all the necessary dependencies. If you don't have conda installed we recommend to install miniforge which is a minimal conda installer. Once installed, you can create the environment by moving to the repository folder and running the following command in the terminal:

conda env create -f binder/environment.yml

To use the environment, you can activate it and start jupyter lab with the following commands:

conda activate EHML2024
jupyter lab

Name		Name	Last commit message	Last commit date
Latest commit History 62 Commits
binder		binder
datasets		datasets
01-ML_introduction.ipynb		01-ML_introduction.ipynb
02-Numpy_basics.ipynb		02-Numpy_basics.ipynb
03-Numpy_indexing.ipynb		03-Numpy_indexing.ipynb
04-Pandas.ipynb		04-Pandas.ipynb
05-Pandas_combine.ipynb		05-Pandas_combine.ipynb
06-Minimal_plotting.ipynb		06-Minimal_plotting.ipynb
07-EDA_first_step.ipynb		07-EDA_first_step.ipynb
08-Linear_regression.ipynb		08-Linear_regression.ipynb
08b-Gradient_descent.ipynb		08b-Gradient_descent.ipynb
09-Multiple_regression.ipynb		09-Multiple_regression.ipynb
10-Logistic_regression.ipynb		10-Logistic_regression.ipynb
11-Nonlinear_validation.ipynb		11-Nonlinear_validation.ipynb
12-Classification.ipynb		12-Classification.ipynb
13-Clustering.ipynb		13-Clustering.ipynb
14-Decision_trees.ipynb		14-Decision_trees.ipynb
15-Time_series.ipynb		15-Time_series.ipynb
README.md		README.md

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