Udemy – Linear Algebra for Data Science and Machine Learning 2025-7

Udemy – Linear Algebra for Data Science and Machine Learning 2025-7 Downloadly IRSpace

Aug 17, 2025 - 02:37 Updated: Aug 19, 2025 - 14:32

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Udemy – Linear Algebra for Data Science and Machine Learning 2025-7

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.

What you will learn

Understand the importance of linear algebra for data science and machine learning.
Explore basic concepts such as scalars, vectors, matrices, and tensors.
Represent data and solve linear systems using algebraic methods.
Identify key properties and perform basic operations with vectors and matrices.
Mastery of linear transformations (such as scaling, rotation, shearing).
Calculating eigenvectors, eigenvalues, and applying matrix decompositions (Eigendecomposition, SVD).
Implement principal component analysis (PCA) for dimensionality reduction.
Coding linear algebra operations in Python using specialized libraries (such as NumPy, SciPy).
Applying linear algebra to real-world machine learning applications.
Reinforce learning through theoretical exercises and practical challenges.

This course is suitable for people who:

Data science, machine learning, and artificial intelligence professionals and students looking to strengthen their mathematical foundations.
Developers and programmers looking to understand and apply linear algebra in Python.
Researchers and academics interested in the mathematical principles behind neural networks and artificial intelligence algorithms.
Data engineers and analysts who need to manipulate, transform, and reduce dimensionality in data sets.
Anyone who wants to understand the mathematical principles of artificial intelligence and apply them practically.