Unsupervised Learning
Applications with Sklearn
This module provides you with a comprehensive overview of the most important unsupervised learning methods in machine learning. Through a series of case studies, we will teach you which models are suitable for specific tasks and how you can effectively design the data preparation for the application of these models. The focus is on clustering, anomaly detection and dimensionality reduction.
What will you learn?
Python frameworks: We start with a refresher of the main Python frameworks used in machine learning, including Sklearn, Pandas and NumPy.
Unsupervised Learning: You will learn about different unsupervised learning models, including clustering methods, such as K-Means, and Principal Component Analysis (PCA).
Training: We explain how you can train these models and apply them in practice.
Optimisation: Finally, we cover methods for optimising the models and evaluating their performance.
Practical relevance: You will work on practical problems using real data.
Duration of the course: 2 days
This course provides you with in-depth knowledge in the field of unsupervised learning, which you can apply directly to your own projects. We offer both theoretical concepts and practical exercises to ensure a comprehensive understanding of these important techniques.
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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