Supervised learning with Sklearn
Applications with Sklearn
In this module, you will gain a thorough overview of the central supervised methods in the field of machine learning. Using practical case studies, we will show you which models are particularly suitable for certain problems and what effective data preparation for the application of these models looks like. The focus of the course is on classification and regression.
What will you learn?
Python frameworks: We start with an overview of the main Python frameworks relevant to machine learning, including Sklearn, Pandas and NumPy.
Supervised Learning: You will learn about different models of supervised learning, including decision trees, linear models and support vector machines.
Training: We explain how different models are trained and discuss the specifics of each.
Optimization: Finally, we cover methods for optimizing these models, evaluating their performance and hyperparameter optimization.
Practical relevance: You will work on practical problems using real data.
Duration of the course: 2 days
This course is a comprehensive introduction to supervised learning with Sklearn and prepares you to use the advantages of these methods effectively in your own projects.
Price/participant: 1049€ plus VAT.
(Benefit from an exclusive discount if you register for several courses).
You can count on it!
Live tutoring
Blended learning
Individual location & time planning
Materials available for download
Certificate of completion
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