Choosing the right algorithm for the right task - Zwijnaarde - Past

In this session, the following questions will be answered: 

  • How to translate your business objective(s) to a data science task?
  • What are the most important data science tasks, and which machine learning algorithms and techniques exist to solve these tasks?
  • How to choose the appropriate algorithm based on important characteristics of the available data and expected model requirements such as accuracy, interpretability, scalability, etc.?
  • How to train and evaluate the resulting models, in order to arrive at the most optimal performance?

The goal of this session is to introduce the participants to the most important data science tasks (classification, clustering, regression, etc.) and provide an overview of the most commonly used algorithms and techniques to solve each of these tasks. For each of the methods, its characteristics, advantages and disadvantages will be explained in order to guide the participants in making a conscious choice in terms of the available data (dimensionality, attribute types, etc.) and the expected model requirements (interpretability, accuracy, scalability, etc.). Finally, the guiding principles to train and evaluate the resulting models, including an overview of common pitfalls and frequently-used evaluation measures, will be presented.

For further information and registration: 

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