Machine Learning: Classification Models

Course overview

Classification models systematically categorize datasets and are a central component of modern data analysis. In everyday work, they often determine priorities and subsequent actions. The course derives the methodological foundations from pandas and classical statistics and complements them with Power BI reporting and strategic AI classification. !Course Content !1. Data Basis for Classification *Reading and checking data with pandas *Identifying missing values *Scale types for classification tasks *Data aggregation and Boolean masking !2. Logistic Regression as a Classifier *Logistic regression with Python *Mean, median, and quartiles as context *Box plots for distribution statements *Scatter plots for class boundaries !3. Classification Results in Power BI *DAX measures for class sizes *Bar and pie charts for distribution *Filter contexts for class perspectives *Trend lines for model observation !4. AI Classification of Classification Models *Use cases for classification-based decisions *Risks and biases in models *Governance in automated classifications *Requirements from the EU AI Act !Professional Relevance *Structured evaluation of categorical data *Robust prioritization in daily business *Connection between model and reporting *Contribution to responsible AI initiatives

Upcoming dates

• 2026-10-25T23:00:00.000Z · 2027-02-20T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-06-19T22:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-02-20T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-06-19T22:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-02-20T23:00:00.000Z · München · Combined Learning