From the course: Machine Learning Foundations: Linear Algebra
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Applications of linear algebra in ML
From the course: Machine Learning Foundations: Linear Algebra
Applications of linear algebra in ML
- [Instructor] Linear algebra is one of the main building blocks of machine learning. At its base, it is a study of vectors, matrices, and linear functions. So you have to be familiar with vectors, matrices, and their characteristics, as you're going to deal with them in ML. What about machine learning algorithms? Without knowing linear algebra, you cannot understand them and therefore cannot change and improve them. Let's explore nine most important applications of linear algebra. Data set and data files. In machine learning we fit the model on a data set. This data set is either a matrix or a vector. For example, our model could be a fitness related model that predicts quality of sleep. Images and photographs. If you're working on a computer vision application, then you're dealing with images or photographs. In this case, you cannot send an image to a model and expect it to understand it. Each image is made of pixels…
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