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The fundamental mathematical tools needed to understand Machine Learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
This self-contained textbook bridges the gap between mathematical and Machine Learning texts, introducing the mathematica.
These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics.
The fundamental mathematical tools needed to understand Machine Learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics
Uneori, aceste descrieri pot contine inadvertente. De asemenea, imaginea este informativa si poate contine accesorii neincluse in pachetele standard.
