Machine Learning and Deep Learning in Business - Intensive

Course overview

The workshop covers the most important applications of machine learning, combined with realistic use cases and datasets. You will learn the following concepts: Algorithms for supervised learning Linear models, k-nearest neighbors, decision trees Support Vector Machines with kernel Unsupervised learning and preprocessing Cluster analysis, DBSCAN, logistic regression, naive Bayes Dimensionality reduction, principal component analysis (PCA) Representation of data and feature generation Evaluation and improvement of models Cross-validation, grid search, evaluation metrics Chained algorithms and pipelines Processing of text data (Natural Language Processing) Neural networks DNN, CNN, RNN, GRU, LSTM, autoencoders, transformers Transfer learning Object detection (Yolo), image segmentation Special networks: U-Net, ResUnet Autoencoders, VAE, GANs, Siamese networks Concrete projects implemented in the course with Python from various industries and business functions Marketing Customer segmentation (k-Means, autoencoders, PCA) Recommendation systems (neural networks) Healthcare Diagnosis of diseases based on symptoms or X-rays (neural networks) Tumor detection and localization (image segmentation, U-Net or ResNet) Sales Demand or revenue forecasting (univariate, multivariate) (LSTM, GRU, transformer) Plant protection Detection of plant diseases (image segmentation using U-Net) Access control Facial recognition (Siamese network or VggFace) Production : Outlier detection (autoencoder, PyOD) Anomalies in quality control (image segmentation with ResNet) Anomalies in curve runs (e.g., pressure or temperature curves) (autoencoder) Automatic detection or counting of objects (e.g., items, pallets, people...) Predictive maintenance Anomaly detection in time series (autoencoder, PyCaret) Preventive maintenance using a turbine engine example (LSTM) Insurance Automatic damage detection (image segmentation with Res-Net, annotation) Other applications created during the training Text summarization (transformer, BERT) Extraction of named entities from documents (transformer, BERT) Email classification (LSTM, BERT) Detection and deciphering of license plates (Yolov7) Evaluation of drone footage (image segmentation, U-Net)

Upcoming dates

• 2026-11-15T23:00:00.000Z · 2026-11-19T23:00:00.000Z · Köln · Seminar
• 2026-11-15T23:00:00.000Z · 2026-11-19T23:00:00.000Z · Online-Seminar
• 2027-02-14T23:00:00.000Z · 2027-02-18T23:00:00.000Z · Online-Seminar
• 2027-02-14T23:00:00.000Z · 2027-02-18T23:00:00.000Z · Köln · Seminar