Description
Linear Algebra for Programmers, Vectors to SVD (Python & C#) This course provides a comprehensive introduction to linear algebra for programmers, artificial intelligence, and machine learning using Python and C#. Linear algebra is one of the most important and fundamental mathematical tools in modern software development. Whether you work in graphics, artificial intelligence, simulations, data science, or game development, people use it constantly, often without direct awareness. Vectors and matrices are present in all areas of technology; from rotating and moving objects on the screen in graphics to neural networks and data visualization in artificial intelligence, physics engines for calculating motion, force, and collisions, as well as camera control, lighting, and animation in game development. Data science also relies heavily on these concepts for regression, clustering, and dimensionality reduction. This course helps learners to understand mathematical concepts in depth and step by step, without the need for a heavy math degree, relying only on curiosity, logic, and programming experience. Each lesson includes clear learning objectives, clear theories with solved examples, real-world applications, practical code samples, practical exercises, educational videos, and comprehension tests. Topics start from the most basic concepts of vectors and matrices and continue to more advanced topics such as LU decomposition, QR decomposition, eigenvalues, eigenvectors, and singular value decomposition (SVD) so that scholars can optimize systems and implement modern AI applications.
What you will learn
- Core concepts of linear algebra: deep understanding of vectors, matrices, rank, spaces, and subspaces.
- Basic operations: Mastering addition, scaling, and internal multiplication in programming codes.
- Graphical transformations and simulations: Using matrices to move and transform objects in computer graphics.
- Applications of Artificial Intelligence: Connecting linear algebra concepts to machine learning and data processing.
- Advanced decomposition techniques: Solve problems using LU, QR, Cholesky, and SVD decomposition for efficient and stable computations.
- Practical Applications: Implementing mathematical concepts using programming languages to solve real-world problems.
This course is suitable for people who:
- Programmers and software developers: People who want to strengthen their mathematical foundation in linear algebra.
- AI and data science professionals: Those looking to learn the mathematical topics underlying machine learning models.
- Graphics and game developers: People who deal with physics engines, animations, and 3D rendering.
- Learners with limited mathematical knowledge: Those who feel that their current mathematical knowledge creates a limitation to progress in modern technologies.
Linear Algebra for Programmers Vectors to SVD (Python & C#) Course Details
- Publisher: Udemy
- Instructor: Sjaak Verwaaijen
- Training level: Beginner to advanced
- Training duration: 7 hours and 11 minutes
- Number of lessons: 33
Course headings
Prerequisites for the Linear Algebra for Programmers Vectors to SVD (Python & C#) course
- No prior knowledge of linear algebra is required. A basic understanding of programming (in Python, JavaScript, or C#) will be helpful, but all mathematical concepts are explained step by step.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 1080p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
5.6 GB