Python

Course overview

Overview of all contents of the Python (AI) training: Python Basics, Programming & AI Introduction to Python as a versatile programming language for Data Science, Machine Learning, and Artificial Intelligence Learning central Python fundamentals such as variables, data types, operators, and logical programming structures Understanding conditions, loops, and functions to develop your own Python programs Positioning Python and AI as important technologies for data-driven applications and modern automation processes Python Programming, Data Structures & Automation Working with lists, tuples, dictionaries, and sets for efficient processing of different data types Developing reusable Python functions for structured and clear programs Applying loops and conditional statements to automate recurring tasks Building fundamental programming skills for professional Python projects and data-based workflows Python, NumPy & Numerical Data Processing Introduction to NumPy as a central Python library for mathematical calculations and data analyses Creating and editing multidimensional arrays for processing large datasets Applying indexing, filtering methods, and mathematical operations with Python and NumPy Preparing structured data for further analyses, Machine Learning, and AI applications Data Analysis with Python, Pandas & Data Science Professional processing and analysis of structured data with Python and Pandas Working with Series and DataFrames as well as linking different datasets through merge, join, and concat operations Cleaning, transforming, and organizing data for meaningful Python analyses Applying Python to investigate time series, data patterns, and complex relationships Python Data Visualization, Matplotlib & Seaborn Creating meaningful charts and visualizations with Python, Matplotlib, and Seaborn Conducting exploratory data analyses (EDA) to identify trends, patterns, and anomalies Visualizing statistical relationships for clear communication of data-driven insights Developing professional data storytelling concepts for Python analyses and data science projects Machine Learning with Python & Artificial Intelligence Introduction to Machine Learning and the development of intelligent models with Python and Scikit-Learn Applying supervised learning methods such as regression, classification, decision trees, and random forests Fundamentals of neural networks, deep learning, and modern AI algorithms for data-driven predictions Developing and evaluating initial machine learning models based on practical Python applications Python, Unsupervised Learning & Data Modeling Applying unsupervised machine learning methods to detect hidden patterns in datasets Using clustering algorithms such as k-Means and DBSCAN for structured data analysis Dimensionality reduction with Principal Component Analysis (PCA) to simplify complex data structures Linking Python, Machine Learning, and AI to develop intelligent analysis methods Python Model Optimization, Feature E…

Upcoming dates

• 2026-10-25T23:00:00.000Z · 2027-02-06T23:00:00.000Z · Köln · Hybrid Learning
• 2026-10-25T23:00:00.000Z · 2027-05-22T22:00:00.000Z · Berlin · Hybrid Learning
• 2026-10-25T23:00:00.000Z · 2027-02-06T23:00:00.000Z · Hamburg · Hybrid Learning
• 2026-11-29T23:00:00.000Z · 2027-03-13T23:00:00.000Z · Köln · Hybrid Learning
• 2026-11-29T23:00:00.000Z · 2027-06-26T22:00:00.000Z · Köln · Hybrid Learning