AI Expert

You will learn to know and effectively use many different artificial intelligence tools and you will get up-to-date information about the latest developments in generative artificial intelligence tools.

You will learn through practical examples and tasks. The tasks cover a wide range of topics, from producing text to teaching and creating materials, program code, images and videos. You can choose which tasks you do, as long as the required minimum amount is met.

The course contains minimal theory, about 99% is practice and 1% theory. The course does not include the study of mathematics.

During the course, you will learn:

- Prompt engineering, i.e., how to write effective prompts to complete a desired task
- How to use image generation tools to create photorealistic images and stunning art
- How to utilize image-to-image generation, as well as image-to-video generation
- How to use AI to create presentation materials and videos
- How to create music using AI
- How to use AI to write, rewrite, and analyze any text
- How to use AI for generating ideas and as your expert assistant
- How to use AI tools for programming tasks (code generation, finding and fixing errors, documentation, testing, code rewriting/refactoring, agent-based AI-assisted software development)

Unlock the power of data with Python - one of the most versatile and widely used programming languages in data science. In this course, you’ll learn how to process, transform, and analyze data step by step, building a strong foundation for real-world data work.

You’ll begin by exploring and visualizing single-variable data before moving on to multi-variable analysis and discovering patterns, trends, and relationships. Through hands-on exercises and practical examples, you’ll also perform statistical tests to reveal dependencies and draw meaningful insights from data.

By the end of this course, you will be able to:
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• Write Python programs and manage data effectively, including reading, writing, and handling various data formats, as well as working with fundamental programming structures.
• Process, clean, and transform data using the pandas library, including handling missing values, duplicates, and applying selection, indexing, grouping, and manipulation techniques to extract meaningful insights.
• Conduct exploratory data analysis and basic statistical testing to investigate relationships between variables and support data-driven decision-making.
• Visualize and communicate data insights effectively using Matplotlib and principles of visual analytics, and integrate all acquired skills into practical projects and real-world workflows.

Along the way, you’ll work with essential Python libraries such as pandas and Matplotlib, gaining skills that extend far beyond analysis — skills you can apply to automation, reporting, data engineering, and more. By the end of the course, you’ll have the confidence to manage data workflows from raw input to actionable insights.

The following topics will be covered:

• Introduction to Python programming structures for data processing
• Reading and writing different data formats
• Understanding and working with data structures in Python
• Data cleaning: handling missing values and duplicates
• Data selection, indexing, grouping, and transformation with pandas
• Exploratory data analysis (EDA) of single and multiple variables
• Visualizing data using Matplotlib and principles of visual analytics
• Performing basic statistical tests to explore relationships between variables
• Applying skills in a practical mini-project integrating all course topics

The main objective is to learn to utilize existing AI-related APIs (Application Programming Interface).

Training your own AI models can be prohibitively expensive and time-consuming. This course focuses on services and APIs (Application Programming Interfaces), which allow you to quickly build your own AI-powered applications or integrate AI into your toolkit or existing applications. The content covers all leading AI product providers (OpenAI, Google Gemini, Anthropic), as well as image generators and audio (speech and music) generation. The course will also extensively cover various open-source AI models, discussing their pros and cons.

The course includes many examples and programming assignments. Some of these may require payment for the use of AI APIs, but the costs are likely reasonable (e.g., about 10 euros can purchase enough credits for short experiments). Some models can be used for free or run on your own computer.

Programming will be done using Python. Students should also be familiar with the basics of Git.

During the course, you will learn:

- How to use over ten different AI-related APIs (Application Programming Interfaces) to leverage existing AI services for creating text, images, audio (speech and music), and more.
- How to process all kinds of data, from PDF files to docx files, websites, images, videos, and speech, as input for Large Language Models (LLMs).
- How to create structured output (JSON) and PDF files using LLMs.
Retrieval-Augmented Generation (RAG) technique.
- How to create your own custom bots and virtual personas using system prompts.
- How to create a modern web user interface or a powerful command-line interface for your AI application.
- How to use AI to speed up and automate tasks such as text summarization, data collection, image creation, audio translation and transcription, creating product descriptions, creating marketing materials, and data analysis.
- How to install and use AI tools on your own computer.

This course introduces machine learning to make predictions based on data. You will get to know several supervised and unsupervised learning algorithms.
Explore the power of machine learning and learn how data can be transformed into accurate predictions and actionable insights. This course introduces key supervised and unsupervised learning techniques, including k-Nearest Neighbors, k-Means clustering, linear regression, decision trees, and support vector machines (SVMs). You’ll also experiment with dimensionality reduction and gain foundational skills in text analysis and sentiment analysis. Through hands-on projects and practical exercises, you’ll develop the ability to design, implement, and deploy predictive models using Python and its powerful data science libraries.

This course introduces the fundamentals of machine learning and how predictive models can be built from data. Students will become familiar with key supervised and unsupervised learning algorithms and learn how to apply them to real-world problems.

After completing this course, you will be able to:

• Demonstrate a solid understanding of core machine learning concepts, techniques, and workflows.
• Apply a range of supervised and unsupervised learning methods to practical predictive tasks.
• Design, implement, and deploy simple machine learning solutions using Python and its powerful data science libraries.

The course covers essential predictive modelling techniques and machine learning foundations, including:

• Supervised learning fundamentals (e.g. k-Nearest Neighbors)
• Unsupervised learning techniques (e.g. k-Means clustering)
• Linear regression and correlation analysis
• Model complexity: underfitting and overfitting
• Decision Trees for classification and regression
• Support Vector Machines (SVMs)
• Dimensionality reduction techniques
• Introduction to text analysis: topic detection, regular expressions, and Natural Language Processing (NLP)
• Sentiment analysis basics

Explore deep learning and discover how machines learn to recognize patterns, images, and data on their own. This course introduces the foundations of artificial neural networks, convolutional neural networks (CNNs), and key optimization techniques such as gradient descent. Through hands-on practice, you’ll train models with real data, experiment with transfer learning, and apply computer vision libraries to solve practical problems. By the end of the course, you’ll have the skills to design, train, and adapt neural network models for real-world applications.

The aim of this course is to provide students with a solid conceptual understanding of neural networks along with hands-on experience in building, training, and applying them to real-world data.

After completing this course, you will be able to:

• Explain the fundamental principles of artificial neural networks and how they learn from data.
• Understand and implement the gradient descent optimization algorithm.
• Design, train, and evaluate convolutional neural networks (CNNs) for image-related tasks.
• Use Python libraries for computer vision and apply them to practical problems.
• Perform model training using custom datasets and apply transfer learning to improve performance.
• Modify and extend existing neural network code to address new datasets and tasks.

The course covers the following key topics:

• Python libraries for computer vision
• Fundamentals of artificial neural networks
• The gradient descent algorithm and model optimization
• Convolutional neural networks (CNNs) and their applications
• Training neural network models with your own data
• Applying transfer learning for improved results

These topics are explored through lectures, selected literature, and extensive Python code examples. Students will work with provided code bases and adapt them to new datasets, forming the basis of the practice project, which is submitted for evaluation at the end of the course.

During the AI project course you will implement and document a project, which utilizes AI heavily. After completing the course, you will be able to identify a real-world problem and propose a solution for it using AI. You know to use tools and methods to design the solution. You implement the solution using a systematic approach. You evaluate the solution in different scenarios, considering possible problems and their mitigation. You present your solution to different stakeholders using appropriate language.

As a group you will implement and document a project, which utilizes AI heavily. The end result can be an entire book, video, game, web site, new mobile, desktop or web application, but it cannot be the result of a single prompt. The project could also be the use of existing AI APIs with robotics, IoT or automation. The project could also be an art installation or a tech prototype for a company.

The project is divided into several phases: forming the group, planning, prototype and final version. The students will receive feedback from each phase.