Machine learning in Python with scikit-learn (Inria) - Achievement badge

Predictive modeling is a pillar of modern data science. In this field, scikit-learn is a central tool: it is easily accessible, yet powerful, and naturally dovetails in the wider ecosystem of data-science tools based on the Python programming language. This course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science. The course is more than a cookbook: it teaches the participants to be critical about each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions. The training is essentially practical, focusing on examples of applications with code executed by the participants. The authors of the course are : - Arturo Amor - Inria, scikit-learn engineer - Loïc Estève - Inria, scikit-learn core developer - Olivier Grisel - Inria, scikit-learn core developer - Guillaume Lemaître - Inria, scikit-learn core developer - Gaël Varoquaux - Inria, scikit-learn project manager The duration of the course is approximately 36 hours.

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