Participants in this training typically have a background in a quantitative subject, analytics or are junior/medior data scientists, who have the ambition to dive deeper into specific machine learning algorithms and techniques.
In order to get most value out of the training, you have basic experience with machine learning in Python. You should have knowledge about the machine learning workflow and familiarity with concepts such as overfitting/underfitting, scoring metrics and parameter tuning. If you previously followed the Basic Machine Learning training these prerequisites are satisfied.
After the training you receive a Certificate of Completion.
This training consists of 1 lesson. Contact us for a tailored training.
We will start with a general introduction, potential applications, and prerequisites for Recommender Systems. We then delve into the different types of models - Popularity-based baseline models, Content-based models, Collaborative Filtering models, and Hybrid models.
After that, we will do several lab exercises where we will apply two different types of Recommender Systems to the MovieLens dataset with movie ratings and compare how they perform.
The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:
After this training you receive a Certificate of Completion.