Jakob Cassiman

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As a machine learning engineer, Jakob builds learning systems in a variety of consulting projects.

Before joining ML6, he studied engineering sciences at KU Leuven and then went on to Carnegie Mellon University for his master’s in Electrical and Computer Engineering. It’s during that time in the US that he fell in love with the wonders of machine learning.

Jakob is also a member of ML6’s Structured Data research chapter and he regularly dives deep into new machine learning papers and technologies. Definitely check out his blog posts on the ML6 blog.

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Our know-how

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Why we open sourced two Dutch summarization datasets

Open Sourcing Dutch Summarization Datasets for Transfer Learning

May 12, 2022

Jakob Cassiman

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Knowledge Graphs: An introduction and business applications

Learn how to structure your data with knowledge graphs in this introduction blogpost to knowledge graphs and its business applications.

October 26, 2021

Jakob Cassiman

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Is NeuralProphet better than Prophet for sales forecasting?

We often get the question to forecast sales demand for customers. Recently we’ve done this successfully using Prophet. A new package called NeuralProphet builds upon this: it adds some neural networks to the mix. In this blog post we compare both time series forecasting models on a real-world dataset. We're predicting B2C sales volumes in retail to find out both methods' strengths and weaknesses.

June 2, 2021

Jakob Cassiman

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How are knowledge graphs and machine learning related?

Learn about three ways that knowledge graphs and machine learning reinforce each other.

January 6, 2022

Jakob Cassiman

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How a pretrained TabTransformer performs in the real world

Pretrained TabTransformer performs well in unsupervised pretraining on tabular data, but LightGBM outperforms it on small labeled datasets.

January 3, 2022

Jakob Cassiman