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Künstliche Intelligenz ONLINE

Möchte dein Kind alles rund ums Thema KI lernen? Dann ist dies der passende Kurs!

Kommen dir Begriffe wie „neuronale Netze“, „maschinelles Lernen“ oder „genetische Algorithmen“ wie alte Zaubersprüche vor? In unseren Kursen wirst du ihre Geheimnisse entschlüsseln und lernen, wie du sie einsetzt, um fortschrittliche KI-Modelle zu entwickeln. Jede Lektion verbindet Theorie mit Praxis und zeigt dir, wie KI die Welt verändert und wie du aktiv Teil dieser Revolution werden kannst. Entdecke, wie es sich anfühlt, die Zukunft mit KI zu gestalten!

einmal pro Woche 1 pro Woche, 2 x 45 Min.

Kleine Gruppen Durchschnittlich 6 Personen in der Gruppe

Zahlung Monatlich, vierteljährlich, jährlich

Preis pro Stunde ab 14,20 €

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Kursinhalte

Haben Sie sich jemals gefragt, wie künstliche Intelligenz funktioniert und wie Programmierer intelligente Anwendungen entwickeln? In unserem Kurs lüften Sie die Geheimnisse der künstlichen Intelligenz, indem Sie die Grundlagen von Python und die neuesten KI-Tools wie TensorFlow erlernen. Sie werden sich wie ein echter Programmierer fühlen und Ihre eigenen Projekte erstellen – von der Bilderkennung über die Emotionsanalyse in Texten bis hin zur Entwicklung textbasierter Rollenspiele.

Kursplan

The aim of the lesson is to introduce participants to the concept of AI. Additionally, the term LLM and tools based on LLM will be presented.
The class will introduce students to prompt engineering and the principles of writing quality prompts.
During this lesson, students will learn about the history of AI. Additionally, they will be introduced to tools for generating images and how to construct prompts for them.
During the class, students will learn what the OpenAI organization is, what GPT models chatGPT provides, and learn about music generation tools.
The goal of the lesson is to introduce students to the risks and ethical dilemmas associated with teaching and using AI.
The goal of the class is to familiarize students with the environments in which we will program during the course (Google Colab, Python and git).
The aim of the course is to repeat information about the NumPy library, deepen your knowledge of it, and present the benefits of using it.
During the classes, students will become familiar with a data visualization tool - the matplotlib library.
During classes, students will be introduced to the pandas library.
During the classes, students will learn what statistics is and how regression works. During the classes, we will also program the first AI model!
During the classes, students will learn what statistics is and how regression works. During the classes, we will also program the first AI model!
In this class, students will learn about classification and create a binary classification system. They will also learn metrics for evaluating the quality of a binary classification model.
In this class, students will learn about classification and create a binary classification system. They will also learn metrics for evaluating the quality of a binary classification model.
The aim of the course is to systematize the knowledge acquired during the implementation of the project - a simulator of the chance of survival on the Titanic!
The aim of the course is to systematize the knowledge acquired during the implementation of the project - a simulator of the chance of survival on the Titanic!
During the classes, students will become familiar with the KNN algorithm and create a program to classify iris species.
During the classes, students will be introduced to the concepts of fuzzy logic, fuzzy sets, and fuzzy reasoning.
During the classes, students will learn how to implement fuzzy set generation and perform fuzzy inference in Python.
During the lesson, students will use the knowledge they have acquired to solve the problem of classifying Irises on their own. Fuzzy logic will be used to solve the problem. The main goal of the project is for students to attempt to solve the problem on their own and evaluate the results.
During the class, students will be introduced to the naive Bayes algorithm, which will be used in future classes to create a spam categorization system.
During the classes, we will prepare a spam classifier using the Naive Bayes algorithm. In the next classes, we will connect the algorithm to a gmail mailbox.
During the lesson, we will integrate the spam filter created last week with the gmail mailbox.
During the classes, students will learn what artificial neurons are and how they work, what deep learning is, and will create their first model of a simple neural network.
During classes, students will build a network that allows for image classification and learn about the elements that make up neural networks.
The aim of the course is to introduce students to the concept and operation of convolution.
The course will present convolutional networks. Students will create a program that classifies graphics.
During the classes, students will learn what overfitting is, what can cause it and how to detect it. Additionally, they will learn about a tool for monitoring the learning of neural networks - tensorboard. In addition, students will be presented with a mechanism that allows improving the quality of data sets - augmentation.
In this course, students will discover how artificial intelligence can create new images by combining elements from two different photographs: one as a source of content and the other as a source of artistic style. They will learn about the concepts of content and style in images and how neural networks can separate and transform them.
The aim of the course is to present to students what natural language processing (NLP) is and how it works.
The goal of this lesson is to expand knowledge about vector representation of texts.
The aim of the course is to learn about fine-tuning during text generation with the GPT-2 model.
In this course, students will discover how artificial intelligence can create new images by combining elements from two different photographs: one as a source of content and the other as a source of artistic style. They will learn about the concepts of content and style in images and how neural networks can separate and transform them.
The aim of the course is to learn and configure a model for generating speech in Python.
The aim of the course is to create a gesture recognition program, collect data, and configure a neural network model.
The aim of the course is to continue the Rock Paper Scissors gesture recognition project using our own MobileNetV2 model training data.
The aim of the course is to create your own project using the artificial intelligence algorithms you have learned.
You will learn how AI is used in medicine. Examples of practical applications.
You will learn how AI can predict trends in financial markets. You will create a predictive model.
You will learn the basics of robotics and create simple control algorithms. It's like playing an engineer.
You will learn how AI can integrate with IoT devices. You will create smart home systems.
You will create a model for face recognition. You will see how AI works in security.
You will learn methods of analyzing large data sets. You will learn how to handle Big Data.
You will see how AI can create art. Generating images and music using AI.
You will learn how AI can assist in education. You will create interactive lessons and quizzes.
You will learn how AI is used in marketing. Creating models that personalize content for users.
You will learn how AI can optimize logistical processes. Creating models that manage deliveries.
You will learn about the latest trends in AI. We will consider what the future holds.