Künstliche Intelligenz

MachineLearnAthon

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Tutorials

Here, we provide an overview of our 29 tutorials that demonstrate how to implement the solutions introduced in the Micro Lectures. Each tutorial showcases different tools and approaches to machine learning implementation.The table below lists all tutorials with their Title, YouTube Link, and corresponding Code. Please note that for some tutorials, no code is provided — for example, those featuring no-code or low-code tools that allow you to build modular ML pipelines by adapting algorithms visually rather than through programming, or tutorials covering topics such as Docker installation, which do not involve code directly.To help you choose the right tool for your own projects, we also offer a detailed Tool List, outlining the advantages and disadvantages of various tools used across our tutorials.

Title	Youtube Link	Code
AutoML frameworks FLAML and MLJAR	Tutorial: AutoML frameworks FLAML and MLJAR	AutoML_frameworks_tutorial
xAI with LIME	Tutorial: xAI with LIME	xAI_with_LIME_ (Local_Interpretable- Model_Agnostic_Explanations)
Clustering	Clustering – Part I Clustering – Part II Clustering – Part III Clustering – Part IV	Clustering
Computer Vision	Computer vision – Tutorial	CV
Docker Desktop Installation Guide	Docker desktop instalation guide
Low Code Classification	Low code classification evaluation Low code classificiation Part1 Low code classificiation Part2 Low code classification Part3 Low code classification Part4
Low Code Regression	Low code regression evaluation Low code regression Part1 Low code regression Part2 Low code regression Part3 Low code regression Part4
No Code Classification	No code classification Part 1 No code classification Part 2 No code classification Part 3
No Code Regression	Tutorial: No code regression Part I Tutorial: No code regression Part II Tutorial: No code regression Part III
Time-Series Forecasting	Tutorial on Time-Series Forecasting – Part I Tutorial on Time-Series Forecasting – Part II	Time_Series_Forecasting_Tutorial
Data Preparation and Visualization	Tutorial on Data preparation and visualization	Data preparation and visualization
LightGBM with Hyperparameter Tuning	Tutorial in LightGBM including hyperparameter tuning	LightGBM with hyperparameter tuning
Random Forest	Tutorial on Random Forest	Random Forest Tutorial

The creation of these resources has been
(partially) funded by the ERASMUS+ grant
program of the European Union under grant
no. 2022-1-DE01-KA220-HED-000086932.

Neither the European Commission nor the
project’s national funding agency DAAD are
responsible for the content or liable for any
losses or damage resulting of the use of
these resources.