Udemy – Mathematics For Machine Learning 2025-3

Updated August 10, 2026 3.3 GB

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Description

Mathematics For Machine Learning. This course provides the mathematical foundations necessary for a deep understanding of machine learning algorithms through a comprehensive presentation. The course consists of four main sections: linear algebra, multivariate calculus, probability and statistics, and optimization methods. In linear algebra, basic concepts such as vectors, matrices, eigenvalues, and singular value decomposition are taught, which are essential for representing and transforming data. Multivariate calculus focuses on the concepts of gradients, Jacobians, and Hessians, which are the basis for optimization techniques used in training models. The Probability and Statistics section also covers random variables, probability distributions, expectation, variance, and statistical inference methods. Optimization methods such as gradient descent will also be explored to understand how models learn from data. Participants will strengthen their problem-solving skills by working on mathematical proofs and deriving formulas. This course allows participants to implement theoretical concepts programmatically by providing hands-on experience using the NumPy and SciPy libraries in Python. The main goal is to develop a solid understanding of the mathematical foundations rather than just the practical application of models. By the end, learners will be equipped with the mathematical and computational tools necessary to derive and implement machine learning techniques from scratch, and will be prepared for advanced studies in the areas of artificial intelligence, data science, and mathematical modeling.

What you will learn

Understanding the mathematics of models:
Familiarity with the mathematics that drives machine learning models.
Established on the foundations of:
Gain a solid foundation in mathematics to advance to more advanced machine learning models.
Overview for professionals:
A review and refresher in mathematics for data scientists.
Deep understanding of models:
Understanding what’s really going on under the hood of these models.

This course is suitable for people who:

Anyone who wants to understand the mathematics behind machine learning models.
Students who are unsure about Data Science as a career and want to give it a serious try without paying college-level tuition.
Data Scientists who need a refresher in mathematics.
Product Managers who want to know how data scientists and machine learning engineers think.
Machine Learning Engineers who know how to deploy models, but want to know what’s really going on underneath the inner layers of these models.