Data Mining & Machine Learning

Course overview

Data Mining: & Machine Learning The unit clarifies the interplay: Data Mining prepares data and features, while Machine Learning (ML) learns models from them. Data Mining provides reproducible data slices; ML delivers trained artifacts and metrics. Data Mining structures workflows to ensure that models are traceable, comparable, and usable for reports. Technical terms are explained precisely, such as regularization, cross-validation, and threshold calibration. !Content Overview !1. Data - Preparation and Slicing !2. Data Mining - Features and Selection !3. ML - Training, Metrics, Comparison !1. Data Slicing and Quality *Define and document Train/Validation/Test *Missing values, outliers, encodings, duplicates *Temporal consistency and segmentation !2. Data Mining - Feature Engineering *Scaling, interactions, text representations (TF-IDF/Embeddings) *Information gain, regularization, leakage prevention *Monitor feature drift !3. ML Models and Evaluation *Baselines: linear models, trees, ensembles *Metrics: F1, ROC-AUC, PR curve, calibration *Error analysis and reporting standards

Upcoming dates

• 2026-11-15T23:00:00.000Z · 2027-01-13T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-11-15T23:00:00.000Z · 2027-01-13T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-11-16T23:00:00.000Z · 2027-05-13T22:00:00.000Z · Berlin · Hybrid Learning
• 2026-11-16T23:00:00.000Z · 2027-05-13T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-12-20T23:00:00.000Z · 2027-02-17T23:00:00.000Z · Berlin · Hybrid Learning

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