https://hub.imt-atlantique.fr/datascience-toolkit/session3/index.html Presentation & objectives This session focuses on applying machine learning techniques to Natural Language Processing (NLP) challenges through hands-on spam detection projects. Students will learn to transform unstructured text data into actionable machine learning solutions while mastering the transition from experimental notebooks to production-ready NLP pipelines. Through practical experience with authentic SMS and email datasets, you will explore the unique challenges of text classification including tokenization strategies, feature extraction techniques, cross-domain adaptation, and the critical importance of proper evaluation in imbalanced text datasets. Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/session3/lesson_1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session3/lesson_1/index.html Duration20 min https://hub.imt-atlantique.fr/datascience-toolkit/session3/practical_1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session3/practical_1/index.html Duration2h30 x 2 AI Banned Introduction This practical session introduces fundamental concepts of Natural Language Processing (NLP) through a hands-on spam detection workshop. You’ll learn to process text data, extract meaningful features, and build machine learning models that can distinguish between spam and legitimate messages across different communication channels. Through this guided notebook experience, you will master essential NLP skills: https://hub.imt-atlantique.fr/datascience-toolkit/session3/practical_2/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session3/practical_2/index.html Duration2h30 x 2 AI Allowed Introduction This practical session challenges you to transform your notebook-based spam detection analysis into a production-ready NLP pipeline. Building on the air quality project architecture, you’ll create a structured, testable, and maintainable text classification system while exploring advanced preprocessing techniques and model optimization strategies. Unlike the previous guided notebook experience, this mini-project emphasizes independent problem-solving and professional software development practices. You’ll apply the architectural principles learned in the air quality sessions to the NLP domain, creating a comprehensive solution that demonstrates your ability to build enterprise-grade machine learning systems.