مبانی ریاضیات برای یادگیری ماشین از Vizuara

عنوان دوره : Vizuara – Mathematical Foundations for Machine Learning

سطح : مبتدی

زمان کل دوره : 27 ساعت و 45 دقیقه

تعداد فايل ها : 24 (1080p: 20، 720p: 4)

سال آموزش : 2026

فایل تمرینی : دارد: IPYNB (8)، TXT (40)

مربی دوره : Dr. Sreedath Panat

نرم افزار : –

زیرنویس انگلیسی : ندارد

Vizuara – Mathematical Foundations for Machine Learning

Master the essential mathematical pillars of machine learning, from linear algebra transformations to integral calculus, designed for the Vizuara AI/Generative AI program.

The course “Mathematical Foundations for Machine Learning” offered in January 2026 is designed to equip students with the essential mathematical tools necessary for the field of machine learning. The course covers critical topics such as the dot product viewed as a linear transformation in linear algebra, and the insightful application of eigenvalues and eigenvectors to elucidate the effects of linear transformations on vector scaling. Students will also delve into the fundamentals of integral calculus, gaining an understanding of both definite and indefinite integrals and their intrinsic relationship with differentiation. This strong foundation in mathematics is essential for learners aiming to specialise in artificial intelligence and machine learning applications. The course is suited for those who are part of Vizuara’s AI/Generative AI program, providing a comprehensive blend of theoretical knowledge and practical exercises. Through recorded lectures, interactive office hours, and hands-on projects, participants will develop a robust comprehension of the mathematical concepts that underpin advances in machine learning. Whether you’re a student looking to deepen your understanding or a professional aiming to strengthen your AI skill set, this course provides the necessary groundwork and support to enhance your competencies in machine learning mathematics.

This course provides a comprehensive exploration of the mathematical concepts that underpin modern machine learning and artificial intelligence.

Participants will bridge the gap between theoretical mathematics and practical implementation through a series of structured lectures, interactive office hours, and hands-on projects.

Learning Outcomes
By the end of this course, you will be able to

Interpret the dot product as a fundamental linear transformation.
Apply eigenvalues and eigenvectors to analyze vector scaling in high-dimensional spaces.
Demonstrate proficiency in both definite and indefinite integration techniques.
Explain the intrinsic relationship between differentiation and integration in machine learning models.
Implement mathematical concepts using Python-based tools and datasets.
Translate theoretical mathematical proofs into functional machine learning algorithms.

Prerequisites
Basic understanding of Python programming
Foundational knowledge of high school algebra and trigonometry
Enrollment in the Vizuara AI/Generative AI program