.ipynb .pdf

Binder

Contents

Examples of ML models

Image source: assets.wordstream.com

There are a lot of different ML algorithms available. As a starting point, Commonly used Machine Learning Algorithms provides an overview.

In the following, we will look a bit closer at six selected ML methods from scikit-learn.

1. K-means Clustering

• Step by Step to Understanding K-means Clustering

• K-Means Clustering

2. K-Nearest Neighbors Classification

• K-Nearest Neighbor

3. Support Vector Machines

• Support Vector Machine — Simply Explained

• SVM (Support Vector Machine) — Theory

4. Decision Trees

• Decision Tree Classifiers Explained

• Decision Tree Classification

5. Random Forest

• Understanding Random Forests

6. Multi-layer Perceptron classifier

• Multilayer Perceptron

• Understanding of Multilayer perceptron (MLP)

Group work

For this purpose, you will work in groups on the respective method and then present your results.

Use the following questions as a guide:

• What is the basic mode of operation of the method?

• For which data and problems is the method suitable?

• What are the strengths and weaknesses of the method?

The links provided above with the respective ML method serve as an introduction.

Theoretical introduction Practical introduction