{"id":12235,"date":"2026-08-10T10:42:57","date_gmt":"2026-08-10T10:42:57","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-mathematics-for-machine-learning-and-ai-i-essentials-2025-6\/"},"modified":"2026-08-10T10:42:57","modified_gmt":"2026-08-10T10:42:57","slug":"udemy-mathematics-for-machine-learning-and-ai-i-essentials-2025-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-mathematics-for-machine-learning-and-ai-i-essentials-2025-6\/","title":{"rendered":"Udemy \u2013 Mathematics for Machine Learning and AI I : Essentials 2025-6"},"content":{"rendered":"
\n

Description<\/span><\/h2>\n

Mathematics for Machine Learning and AI I: Essentials. This course provides a comprehensive foundation in the mathematical concepts needed to understand and effectively apply machine learning and AI methods. The course begins with a tutorial on linear algebra, covering vectors, subspaces, eigenvalues, and orthogonality, and demonstrates their practical applications in machine learning algorithms using Python and MATLAB. It then moves on to multivariable calculus, examining functions, derivatives, partial derivatives, Hessian matrices, and Lagrange multipliers, which are critical to the learning and optimization process of models. The next section of the course focuses on probability and statistics, covering the basic concepts of probability, distributions, Bayes\u2019 theorem, hypothesis testing, and mathematical expectation maximization, which are essential for probabilistic reasoning in AI systems. Finally, optimization topics including gradient descent, RMSProp, AdaGrad, KKT conditions, and linear programming are taught to provide a thorough understanding of how to train, constrain, and improve models. With a clear and practical structure, this course teaches both the theoretical aspects and practical implementation of concepts without requiring a specific background in programming or machine learning, and in the end, equips learners with the mathematical skills necessary to advance in the field of machine learning and artificial intelligence.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n