Bart Baesens

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Machine Learning Essentials (E-learning)

🌍 English

In this course, participants learn the essentials of Machine Learning. We start with an introduction to machine learning and its applications. We then discuss data preprocessing and feature engineering. Both are essential steps to build high-performing machine learning models. This is followed by introducing the basic concepts of regression and classification. We then discuss how to measure the performance of predictive analytics techniques. Next, we zoom in on association rules, sequence rules and clustering. We then elaborate on advanced machine learning techniques such as neural networks, support vector machines and ensemble models. We also review Bayesian networks as probabilistic white box machine learning models. A next section reviews variable selection. We extensively discuss machine learning model interpretation and deployment. The course concludes by highlighting some machine learning pitfalls. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples. The course also features code examples in both R and Python. Throughout the course, the instructors also extensively report upon their research and industry experience.

The course features more than 8 hours of video lectures, multiple multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

👩‍🏫 Lecturers

Prof. dr. Bart Baesens
Professor at KU Leuven

Prof. dr. Tim Verdonck
Professor at University of Antwerp

🏢 Location

Anywhere (e-learning).

🏫 Organizer

BlueCourses

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