{"id":11571,"date":"2026-08-10T10:34:02","date_gmt":"2026-08-10T10:34:02","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-linear-algebra-for-data-science-and-machine-learning-2025-7\/"},"modified":"2026-08-10T10:34:02","modified_gmt":"2026-08-10T10:34:02","slug":"udemy-linear-algebra-for-data-science-and-machine-learning-2025-7","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-linear-algebra-for-data-science-and-machine-learning-2025-7\/","title":{"rendered":"Udemy \u2013 Linear Algebra for Data Science and Machine Learning 2025-7"},"content":{"rendered":"
\n

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

Linear Algebra for Data Science and Machine Learning. This course teaches the fundamental concepts of linear algebra in a practical and hands-on manner for use in these fields. Divided into six sections, the course begins with an introduction to basic concepts such as scalars, vectors, matrices, and tensors and explains their relationship to data science and machine learning. The second section examines the properties of vectors, including norms, unit vectors, and orthogonal vectors. The third section discusses matrices and concepts such as transpose, inverse, and matrix decomposition, which are used in neural networks and dimensionality reduction. The fourth section covers matrix operations such as inner and outer multiplication. The fifth section teaches linear transformations, eigenvalues, and decompositions such as SVD and PCA. The final section discusses practical applications of these concepts in solving real-world problems, implementing linear systems, and analyzing data. By combining theory and implementation in Python, this course provides a solid foundation for work in the fields of data science and machine learning.<\/span><\/p>\n

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