Einführung in das Maschinelle Lernen

Code-Repository für den Kurs "Einführung in das Maschinelle Lernen" an der Hochschule Karlsruhe.

Setup

The recommended way to setup your development environment is to use Anaconda:

Download and install Miniconda for your OS from here: https://docs.conda.io/en/latest/miniconda.html
Start the conda terminal and create a new environment for this course:

conda create --name ml-course python=3.8

conda activate ml-course

conda install numpy pandas matplotlib scikit-learn jupyter

Name		Name	Last commit message	Last commit date
Latest commit History 27 Commits
data		data
solutions		solutions
.gitignore		.gitignore
0_PandasIntro.ipynb		0_PandasIntro.ipynb
1_Lineare_Regression.ipynb		1_Lineare_Regression.ipynb
2_Logistische_Regression_Digits.ipynb		2_Logistische_Regression_Digits.ipynb
3_ML-Workflow.ipynb		3_ML-Workflow.ipynb
4_OverUnderfitting.ipynb		4_OverUnderfitting.ipynb
5_Entscheidungsbaeume.ipynb		5_Entscheidungsbaeume.ipynb
6_TreeEnsembles.ipynb		6_TreeEnsembles.ipynb
7_Clustering.ipynb		7_Clustering.ipynb
LICENSE		LICENSE
P1_Pandas_Exercise.ipynb		P1_Pandas_Exercise.ipynb
P2_Logistische_Regression.ipynb		P2_Logistische_Regression.ipynb
P3_ML_Workflow.ipynb		P3_ML_Workflow.ipynb
P4_OverUnderfitting.ipynb		P4_OverUnderfitting.ipynb
P5_TreeEnsembles.ipynb		P5_TreeEnsembles.ipynb
README.md		README.md