Cainã Max Couto da Silva - Intro to ML: How to Prevent Data Leakage and Build Efficient Workflows
www.pydata.org
This webinar will introduce machine learning pipelines and discuss their importance in building efficient and robust workflows. It will explain how pipelines help to prevent data leakage and ensure model stability by allowing for proper separation of training, validation, and test data. Through a blend of theory and practice, it will provide and explain code chunks in Python using well-known open-source packages like scikit-learn (pipeline and column transformers) and feature-engine to ensure a complete understanding of the .fit(), .transform(), and .predict() methods. By the end of this webinar, the audience will have a solid understanding of the theory behind machine learning pipelines and practical examples of using them effectively in their projects.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
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