Jurij Schlaht

Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn
Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Build and train a neural network with TensorFlow to perform multi-class classification
Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
Build and use decision trees and tree ensemble methods, including random forests and boosted trees

4 Webinare in der Serie “Data Science in der Praxis“
– Die ersten Schritte zum Data-Driven Professional
– Daten verstehen, bewerten und aufbereiten
– Predictive Analytics mit Clustering und Forecasting
– Klassifikatiossysteme für die Datenanalyse

What generative AI is and how it works, its common use cases, and what this technology can and cannot do.
How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts.
The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society.