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AI220: Introduction to Machine Learning

Training: Artificial Intelligence

Participants receive a practical introduction to core machine learning techniques such as classification, regression, and forecasting. The course covers how to identify patterns in data and generate accurate predictions. It also addresses the use of established Python libraries such as pandas and scikit-learn through practical use cases.

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Appointments in German language


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Possible course languages: German

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Agenda:

Introduction to Machine Learning: Terms, problem definitions and use cases (classification, regression, prediction, etc.)

Python tools for Machine Learning: Introduction to the usage of Python packages such as pandas and scikit-learn for implementing Machine Learning techniques

Classification techniques: Introduction to various classification techniques, such as Random Forests, kNN, etc., and their application to use cases

Regression techniques: Introduction to various regression techniques, such as Linear Regression, and their application to use cases

Final discussion: Exchange of experience and discussion on how the learned content can be applied in one's own context

Objectives:

Classify concepts from Machine Learning and apply fundamental techniques in real use cases.

Target audience:

Developers
IT Professionals

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)

Description:

In the workshop AI220 Introduction to Machine Learning, participants will learn a series of core techniques that are applied in every data analysis application. Through the introduction of important fundamental concepts and techniques using meaningful use cases, participants will come into contact with various areas of machine learning, such as classification, regression, and prediction.

Learn how to uncover hidden patterns in data and make precise predictions. Utilize innovative techniques that enable revolutionary advances in many industries. To deliver this content efficiently and sustainably, the course particularly focuses on the use of established Python libraries such as pandas and scikit-learn.