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AI300: AI Technology Implementer

Training: Artificial Intelligence

This course offers a practical introduction to data work and AI with Python—from programming basics and data preparation to initial machine and deep learning models. The focus is on hands-on learning with short inputs, exercises in notebooks, mini-cases, and best practices.

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Possible course languages: German

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Agenda:

Introduction to Python for Data Science & AI
- Python fundamentals (syntax, data types, control flow, functions) using simple examples
- Working with libraries & package management (basic principles, installation, usage)
- Initial data analysis with pandas and simple visualizations
- Transfer: application ideas in own environment

Data Extraction & data preparation
- Data formats & typical challenges (CSV/Excel/Logs/JSON, data types, encoding, time formats)
- Parsing and extraction principles
- Data via APIs/web sources
- Preparation & cleaning with pandas: transformations, missing values, duplicates, plausibility checks

Introduction to Machine Learning
- ML fundamentals: problem types, workflow, typical use cases (classification/regression)
- Implementation in Python (training, evaluation, pipelines)
- Compare, interpret and correctly evaluating models (metrics, overfitting, avoid leakage)

Introduction to Deep Learning & AI
- Basic concepts of neural networks: architecture, training, typical pitfalls
- Implementation of simple networks with a common framework (e.g. Keras/TensorFlow or PyTorch)
- Transfer learning as pragmatic introduction (using/adapting pre-trained models)
- Practical perspective: limitations, data/compute requirements, responsible usage (bias/privacy awareness)
- Conclusion: how to transfer learned knowledge as end-to-end approach into daily practice

Objectives:

Be proficient enough in Python to implement data tasks independently and understand other people's code
Robustly import, standardize, and prepare data from typical sources (including error handling/checks)
Apply the standard ML workflow in practice: train, evaluate, improve, recognize pitfalls
Understand the basic principles of deep learning and classify simple models/transfer learning appropriately
Develop a clear understanding of what is realistic in your own context (use cases, data requirements, effort, risks)

Target audience:

The training AI300 AI Technology Implementer is targeted at:

Beginners in Python with a focus on data: data analysts, BI/reporting, (junior) data science, (junior) data engineering
Departments that want to process/evaluate data themselves (e.g., controlling, operations, marketing analytics)
Product/project roles that want to better understand, assess, and support AI projects

Prerequisites:

For participation in the course AI300 AI Technology Implementer, no special prerequisites are required.

Description:

The course AI300 AI Technology Implementer provides a comprehensive, practical introduction to working with data and AI in Python – from solid programming fundamentals to reading, understanding, and preparing data, to initial machine learning and deep learning models.

The didactic focus is on hands-on learning: short inputs, direct exercises in notebooks, small mini cases, and recurring best practices (readability, reproducibility, error handling, clean data transfers). At the end of each module, there is a structured reflection (“What fits our context?”) – and throughout the entire course a small portfolio of notebooks/artifacts is created, which serves as a foundation for further work in daily operations.