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Extra
Part1-intro
Part2-classification
Part3-training_models
Part4-tree_based_models
Part5-neural_networks
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0.0-test.ipynb
0.0-test_data.csv
README.md
environment.yml

README.md

DAT158 image

DAT158ML

Code and documentation for DAT158-ML at HVL. Go to Canvas for information about the course.

You can browse the course notebooks interactively here: https://mybinder.org/v2/gh/alu042/DAT158ML/master

Binder

Installation

We recommend that you install Python through Anaconda. Choose "Python 3.6 version". Test your installation by running python --version in a terminal. The output should contain "Python 3.6" and "Anaconda".

After you've made sure that Anaconda is installed, go through the following steps:

Download the repository:

git clone https://github.com/alu042/DAT158ML
cd DAT158ML

Configure the Python environment

conda env update

Activate environment:

conda activate dat158

If you're using Linux or MacOS and the above command fails, run

source ~/.bash_profile

and try conda activate dat158 again. If that fails, use source activate dat158 instead.

Install a Jupyter kernel

python -m ipykernel install --user --name dat158 --display-name "DAT158"

...and add collapsible headings in the notebooks (not necessary, but convenient)

jupyter nbextension enable collapsible_headings

Test your installation

Run through the notebook 0.0-test.ipynb:

jupyter notebook

You can also use JupyterLab: jupyter lab.

Update

The code and environment will be updated throughout the course. Run the following two commands regularly:

  • Update code: git pull
  • Update environment: conda env update

Troubleshooting

  • If you use Windows and get an error on the form ImportError: DLL load failed: The operating system cannot run %1, try changing the installed version of intel-openmp by running conda install -c defaults intel-openmp -f in the DAT158 environment. Make sure you're in the correct environment (first do a conda activate dat158).
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