# ClassicalMachineLearning

[Machinelles Lernen](https://de.wikipedia.org/wiki/Maschinelles_Lernen) : Basierend auf vorhandenen Daten wird ein [statistisches Modell](https://de.wikipedia.org/wiki/Statistisches_Modell) aufgebaut und getestet. Dies kann dann genutzt werden um im Produktiv-Einsatz Entscheidungen zu treffen.

![https://datasolut.com/machine-learning-vs-deep-learning/](https://4092223458-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MjY9ZUOIiOq3c33tSsV%2Fuploads%2FoG0nCr9Za09eUPnm7ZwI%2Fimage.png?alt=media\&token=1391cbe3-b45e-4caf-973d-f9b996915349)

![](https://4092223458-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MjY9ZUOIiOq3c33tSsV%2Fuploads%2FKvjEQK41uVWK1n8Qpe24%2Fimage.png?alt=media\&token=29c7a914-a6c1-45b9-b63b-ef917223160d)

scikit-learn:

* Simple and efficient tools for predictive data analysis
* Accessible to everybody, and reusable in various contexts
* Built on NumPy, SciPy, and matplotlib
* Open source, commercially usable - BSD license

{% embed url="<https://scikit-learn.org/stable/user_guide.html>" %}

Numpy:

The fundamental package for scientific computing with Python

<https://numpy.org/learn/>

Pandas:

**pandas** is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the [Python](https://www.python.org) programming language.

<https://pandas.pydata.org/docs/user_guide/index.html>