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Les notebooks de ce dépôt sont mes notes de travaux pratiques sur Python, NumPy et les Mathématiques pour le Machine Learning. \n\n## Les sujet abordés\nLes sujets abordés sont les suivants:\n- Un niveau de maîtrise avancé des listes en Python (Slicing / List-Comprehension / Multi-Level Indexing)\n- Algèbre Linéaire en Python (Vecteurs / Matrices / Tenseurs / Tensorflow / Pytorch)\n- Les fonctions indispensables de NumPy pour le Machine Learning\n- Utilisation de Sklearn datasets\n- Les fonctions mathématiques usuelles et les dérivées de fonctions à une variable pour le Machine Learning\n- Utilisation de SymPy pour calculer les dérivées partielles de fonctions à deux variables, le gradient, le lapacien, la divergence et le rotationnel de fonctions à deux ou trois variables\n- Les statistiques\n- Les probabilités\n- Les outils graphiques (Matplotlib / Seaborn / Plotly / PyPlot)\n\n## Conclusion\nCes travaux pratiques m'ont permis de consolider mes connaissances en Python, NumPy et les Mathématiques et Statistiques pour le Machine Learning.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcharlenry%2Fpython_math_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcharlenry%2Fpython_math_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcharlenry%2Fpython_math_machine_learning/lists"}