Applications in Machine Learning

Machine learning is deeply interwoven with linear algebra, as many of its algorithms rely on the manipulation and transformation of vectors and matrices. This chapter focuses on the practical applications of linear algebra within the field of machine learning. You will discover how these mathematical tools are utilized to construct models, optimize algorithms, and process complex datasets.

Throughout this chapter, you will gain insights into how linear algebra underpins popular machine learning techniques such as linear regression, principal component analysis, and support vector machines. Concepts like matrix factorization and eigenvectors will be highlighted to demonstrate how they enable efficient data processing and feature extraction.

By the end of this chapter, you'll have a clearer understanding of how linear algebraic operations support the design and implementation of machine learning algorithms, equipping you to apply these concepts to real-world data science problems.