Recommender Systems Specialization

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Amazon works with it on a daily basis for years: “Customers who bought this, also bought….”. Facebook and LinkedIn give you friend suggestions. Netflix recommends you what to watch. Closer to home, Bol.com and Coolblue try to tempt you into buying similar products when you browse their web shops. All of this is the work of Recommender Systems. An essential tool for companies that strive to offer personalization on a global scale. A good Recommender System will improve engagement, make people feel at home on your website and help them shop more.

The Recommender System training is perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Close the Gap with the Recommender Systems training

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Who should take this course?

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. 

Training Dates

This training consists of 1 lesson. Contact us for a tailored training.

Description of the 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:

  • Applications of Recommender systems
  • Required data and common considerations
  • Types of Recommender systems
  • Popularity model
  • Content-based models
  • Collaborative filtering
  • Hybrid models
  • Matrix factorization methods
  • Dealing with changing contexts
  • Exploitation vs exploration trade-off
  • Performance evaluation: offline & A/B testing
  • Evaluation metrics such as precision@k and recall@k
  • Training and evaluating Recommender systems with LightFM

After this training you receive a Certificate of Completion.