Afficher la pageAnciennes révisionsLiens de retourAjouter au livre.Exporter en PDFHaut de page Cette page est en lecture seule. Vous pouvez afficher le texte source, mais ne pourrez pas le modifier. Contactez votre administrateur si vous pensez qu'il s'agit d'une erreur. =====Course unit: Data science project ===== ==== Course metadata ==== * Title in French: Projet data * Course code: tba * ECTS credits: 3 * Teaching hours: 60h * Type: advanced course * Language of instruction: French * Coordinator: tba * Instructor(s): Alexandre Chirié (Mantiks), Maximilien Defourné (Mantiks) * //Last update 27/08/2021 by C. Pouet// ==== Brief description ==== The course consists of a theoretical part and a practical part, simulating a business project. ==== Learning outcomes ==== * Understand the workflow of a data science project in a business context * Be able to account for business (collection of needs, project lifecycle, communication) and technical (data, machine learning, scaling) constraints ==== Course content ==== - Data science in business * The main issues * Examples of data project - Starting a data science project * The constraints of data science projects * Finding data * Acquiring information * Playing with data - Lifecycle of a project * The Bias-Variance tradeoff * Feature Selection * Feature Engineering * Defining a metric - The basic models * Regressions (linear, polynomial, penalized et logistic) * Decision trees (random forest and gradient boosting) - Focus Natural Language Processing (NLP) * Word Embedding * Example: Sentiment analysis ==== Bibliography ==== Check the availability of the books below at [[https://documentation.centrale-marseille.fr/|Centrale Marseille library]]. * Zeng, A and Casari, A. Feature Engineering for Machine Learning. O'Reilly Media. * Müller, A. and Guido, S. Introduction to Machine Learning with Python. O'Reilly Media. en/ddefiprod.txt Dernière modification : 2021/08/27 10:27de cpouet