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    "# Examples of ML models"
   ]
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    "![](images/machine-learning1.png) Image source: [assets.wordstream.com](https://assets.wordstream.com/s3fs-public/styles/simple_image/public/images/machine-learning1.png?SnePeroHk5B9yZaLY7peFkULrfW8Gtaf&itok=yjEJbEKD)"
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    " There are a lot of different ML algorithms available.\n",
    " As a starting point, [Commonly used Machine Learning Algorithms](https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms) provides an overview.\n"
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    "In the following, we will look a bit closer at six selected ML methods from [scikit-learn](https://scikit-learn.org/stable).\n",
    "\n",
    "## 1. [K-means Clustering](https://scikit-learn.org/stable/modules/clustering.html#k-means)\n",
    "- [Step by Step to Understanding K-means Clustering](https://medium.com/data-folks-indonesia/step-by-step-to-understanding-k-means-clustering-and-implementation-with-sklearn-b55803f519d6)\n",
    "- [K-Means Clustering](https://medium.datadriveninvestor.com/k-means-clustering-c92463d5fa0e)\n",
    "\n",
    "## 2. [K-Nearest Neighbors Classification](https://scikit-learn.org/stable/modules/neighbors.html#classification)\n",
    "- [K-Nearest Neighbor](https://medium.com/swlh/k-nearest-neighbor-ca2593d7a3c4)\n",
    "\n",
    "## 3. [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html#svm)\n",
    "- [Support Vector Machine — Simply Explained](https://towardsdatascience.com/support-vector-machine-simply-explained-fee28eba5496)\n",
    "- [SVM (Support Vector Machine) — Theory](https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72)"
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    "## 4. [Decision Trees](https://scikit-learn.org/stable/modules/tree.html#tree)\n",
    "- [Decision Tree Classifiers Explained](https://medium.com/@borcandumitrumarius/decision-tree-classifiers-explained-e47a5b68477a)\n",
    "- [Decision Tree Classification](https://medium.com/swlh/decision-tree-classification-de64fc4d5aac)\n",
    "\n",
    "## 5. [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest)\n",
    "- [Understanding Random Forests](https://medium.com/@harshdeepsingh_35448/understanding-random-forests-aa0ccecdbbbb)\n",
    "\n",
    "## 6. [Multi-layer Perceptron classifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier)\n",
    "- [Multilayer Perceptron](https://medium.com/@jorgesleonel/multilayer-perceptron-6c5db6a8dfa3)\n",
    "- [Understanding of Multilayer perceptron (MLP)](https://medium.com/@AI_with_Kain/understanding-of-multilayer-perceptron-mlp-8f179c4a135f)"
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    "## Group work\n",
    "\n",
    "**For this purpose, you will work in groups on the respective method and then present your results.**\n",
    "\n",
    "Use the following questions as a guide:\n",
    "- What is the basic mode of operation of the method?\n",
    "- For which data and problems is the method suitable?\n",
    "- What are the strengths and weaknesses of the method?\n",
    "\n",
    "The links provided above with the respective ML method serve as an introduction."
   ]
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