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<html lang="de" style="height:100%"><head><meta charSet="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.3.1/css/bootstrap-reboot.min.css"/></head><body style="height:100%"><div style="height:100%;display:flex;align-items:stretch"><nav style="flex-basis:320px;overflow:auto;padding-top:4em;padding-left:1em;padding-right:1em;padding-bottom:0.5em;margin-bottom:0.5em"><div><a href="index-de.html">Home</a></div><section><h1>Python und Data Science <small>(<a href="python-and-data-science-all-collection-de.html">show individually</a> | <a href="python-and-data-science-all-de.html">show individual overview</a>)</small></h1><section><h1>Python und Data Science: Überblick <small>(<a href="python-data-science-0-overview-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-0-overview-de.html#/0" target="content">Python und Data Science: Überblick</a></li><li><a href="python-data-science-0-overview-de.html#/1" target="content">Pakete</a></li><li><a href="python-data-science-0-overview-de.html#/2" target="content">Jupyter und IPython</a></li><li><a href="python-data-science-0-overview-de.html#/3" target="content">Vertiefung: Anaconda</a></li><li><a href="python-data-science-0-overview-de.html#/4" target="content">NumPy: Überblick und Demo</a></li><li><a href="python-data-science-0-overview-de.html#/5" target="content">Pandas: Überblick und Demo</a></li><li><a href="python-data-science-0-overview-de.html#/6" target="content">Pyplot: Überblick und Demo</a></li><li><a href="python-data-science-0-overview-de.html#/7" target="content">Scikit-learn: Überblick und Demo</a></li></ul></section><section><h1>Python und Data Science: NumPy <small>(<a href="python-data-science-1-numpy-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-1-numpy-de.html#/0" target="content">NumPy</a></li><li><a href="python-data-science-1-numpy-de.html#/1" target="content">NumPy</a></li><li><a href="python-data-science-1-numpy-de.html#/2" target="content">Mehr als ein Weg</a></li><li><a href="python-data-science-1-numpy-de.html#/3" target="content">Numerische Typen</a></li><li><a href="python-data-science-1-numpy-de.html#/4" target="content">Floats in IEEE 754</a></li><li><a href="python-data-science-1-numpy-de.html#/5" target="content">Array Typen</a></li><li><a href="python-data-science-1-numpy-de.html#/6" target="content">Arrays erstellen</a></li><li><a href="python-data-science-1-numpy-de.html#/7" target="content">Auswählen von Array-Einträgen</a></li><li><a href="python-data-science-1-numpy-de.html#/8" target="content">Operationen auf Arrays</a></li><li><a href="python-data-science-1-numpy-de.html#/9" target="content">Fortgeschrittenes Indexing und Filtering</a></li><li><a href="python-data-science-1-numpy-de.html#/10" target="content">NumPy Fortgeschritten</a></li><li><a href="python-data-science-1-numpy-de.html#/11" target="content">Lineare Algebra</a></li></ul></section><section><h1>Python und Data Science: Pyplot <small>(<a href="python-data-science-2-pyplot-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-2-pyplot-de.html#/0" target="content">Pyplot</a></li><li><a href="python-data-science-2-pyplot-de.html#/1" target="content">Plotting</a></li><li><a href="python-data-science-2-pyplot-de.html#/2" target="content">Pyplot: Konfiguration und Styling</a></li><li><a href="python-data-science-2-pyplot-de.html#/3" target="content">Pyplot API</a></li><li><a href="python-data-science-2-pyplot-de.html#/4" target="content">Gundlegende Plots</a></li><li><a href="python-data-science-2-pyplot-de.html#/5" target="content">Visualisierung von Iris-Daten</a></li><li><a href="python-data-science-2-pyplot-de.html#/6" target="content">Figure, Axes & Subplots</a></li><li><a href="python-data-science-2-pyplot-de.html#/7" target="content">Weitere Plots</a></li><li><a href="python-data-science-2-pyplot-de.html#/8" target="content">Anzeigen von Bildern</a></li></ul></section><section><h1>Python und Data Science: Pandas <small>(<a href="python-data-science-3-pandas-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-3-pandas-de.html#/0" target="content">Pandas</a></li><li><a href="python-data-science-3-pandas-de.html#/1" target="content">Pandas</a></li><li><a href="python-data-science-3-pandas-de.html#/2" target="content">Daten importieren und exportieren</a></li><li><a href="python-data-science-3-pandas-de.html#/3" target="content">Beispieldaten</a></li><li><a href="python-data-science-3-pandas-de.html#/4" target="content">Pandas und NumPy</a></li><li><a href="python-data-science-3-pandas-de.html#/5" target="content">Statistische Grundwerte</a></li><li><a href="python-data-science-3-pandas-de.html#/6" target="content">Daten auslesen: Grundlagen</a></li><li><a href="python-data-science-3-pandas-de.html#/7" target="content">Daten auslesen: Fortgeschritten</a></li><li><a href="python-data-science-3-pandas-de.html#/8" target="content">Daten manipulieren</a></li><li><a href="python-data-science-3-pandas-de.html#/9" target="content">Fehlende Daten</a></li><li><a href="python-data-science-3-pandas-de.html#/10" target="content">Plotting</a></li><li><a href="python-data-science-3-pandas-de.html#/11" target="content">Plotting: Beispiele und Übungen</a></li><li><a href="python-data-science-3-pandas-de.html#/12" target="content">Plotting: Scatter Matrix</a></li><li><a href="python-data-science-3-pandas-de.html#/13" target="content">Zeitreihen</a></li><li><a href="python-data-science-3-pandas-de.html#/14" target="content">Gruppierung und Aggregation</a></li><li><a href="python-data-science-3-pandas-de.html#/15" target="content">Multi-Index</a></li><li><a href="python-data-science-3-pandas-de.html#/16" target="content">Joins</a></li></ul></section><section><h1>Python und Data Science: Machine Learning: Theorie <small>(<a href="python-data-science-4-machine-learning-theory-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-4-machine-learning-theory-de.html#/0" target="content">Machine Learning: Theorie</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/1" target="content">Überblick über Methoden</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/2" target="content">Beispiele für Datensätze und Aufgaben</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/3" target="content">Libraries</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/4" target="content">Überwachtes Lernen</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/5" target="content">Iris Datensatz</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/6" target="content">Überwachtes Lernen in Python</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/7" target="content">Beispiel: Iris-Klassifikation</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/8" target="content">Algorithmen für überwachtes Lernen</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/9" target="content">Lineare Regression</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/10" target="content">Neuronale Netzwerke</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/11" target="content">Klassifizierungsalgorithmen</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/12" target="content">Beispiel: Iris-Klassifizierung mit verschiedenen Algorithmen</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/13" target="content">Daten vorbereiten</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/14" target="content">Beispiel: Laden und Vorbereiten von Daten</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/15" target="content">Modellvalidierung und -auswahl</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/16" target="content">Grundlegende Validierungsmetriken</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/17" target="content">Klassifizierungsmetriken</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/18" target="content">Overfitting</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/19" target="content">Beispiel: Iris-Validierung in scikit-learn</a></li><li><a href="python-data-science-4-machine-learning-theory-de.html#/20" target="content">Iris-Klassifikation in scikit-learn - komplett</a></li></ul></section><section><h1>Python und Data Science: Supervised Learning mit scikit-learn <small>(<a href="python-data-science-5-supervised-learning-with-scikit-learn-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/0" target="content">Überwachtes Lernen mit scikit-learn</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/1" target="content">Überwachtes Lernen in scikit-learn</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/2" target="content">Beispiel: Iris-Klassifikation</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/3" target="content">Beispiel: Ziffernerkennung</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/4" target="content">Daten vorbereiten</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/5" target="content">Pipelines</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/6" target="content">Speichern und Laden von Modellen</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/7" target="content">Supervised Learning Algorithmen in scikit-learn</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/8" target="content">Lineare Regression mit scikit-learn</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/9" target="content">Polynomiale Regression mit scikit-learn</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/10" target="content">Regression mittels neuronalem Netzwerk</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/11" target="content">Validierung</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/12" target="content">Abstraktion</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/13" target="content">Beispiel: Gesichtserkennung mit neuronalen Netzen</a></li><li><a href="python-data-science-5-supervised-learning-with-scikit-learn-de.html#/14" target="content">Übungen und Datensätze</a></li></ul></section><section><h1>Python und Data Science Neuronale Netze mit Keras: Überblick <small>(<a href="python-data-science-6-neural-networks-with-keras-collection-de.html">show individually</a>)</small></h1><ul><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/0" target="content">Neuronale Netze mit Keras: Überblick</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/1" target="content">TensorFlow und Keras</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/2" target="content">Beispiel: Iris-Klassifizierung in Keras</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/3" target="content">Neuronale Netzwerke</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/4" target="content">Layer</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/5" target="content">Sequenzielles und funktionales API</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/6" target="content">Beispiel: MNIST Ziffernklassifikation</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/7" target="content">Inspizieren des Iris-Klassifikationsnetzes</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/8" target="content">Beispiel: Werte von Häusern in Kalifornien</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/9" target="content">Beispiel: Ziffernerkennung</a></li><li><a href="python-data-science-6-neural-networks-with-keras-de.html#/10" target="content">Beispiel: Bild-Klassifizierung: Katze oder Hund</a></li></ul></section></section></nav><main 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