Machine Learning Foundations: Linear Algebra
Machine Learning Foundations: Linear Algebra
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 20m | 318 MB
Instructor: Terezija Semenski
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 20m | 318 MB
Instructor: Terezija Semenski
Ever wondered what’s really going on underneath a machine learning algorithm? The answer is linear algebra. Without it, machine learning can’t exist. Linear algebra is a prerequisite for understanding and creating nearly all machine learning algorithms, especially those that prop up neural networks, natural language processing tools, and deep learning models.
Join instructor Terezija Semenski for an in-depth exploration of the core concepts of linear algebra alongside the techniques needed to design and implement a successful machine learning algorithm. Discover the basics of vector arithmetic, vector norms, matrix properties, advanced operations, matrix transformation, and algorithms like Google PageRank. By the end of this course, you’ll be ready to take the principles of linear algebra and apply them to your next big machine learning project.
Machine Learning Foundations: Linear Algebra
