Build a solid foundation in linear algebra concepts essential for understanding and implementing machine learning algorithms. This course covers vectors, matrices, linear systems, vector spaces, eigenvalues, eigenvectors, and matrix decompositions like SVD, focusing on their application in ML contexts using Python libraries like NumPy.
Prerequisites Basic Math and Python
Level:
Vector and Matrix Operations
Perform vector and matrix operations using NumPy and understand their geometric interpretations relevant to data representation and transformation.
Solving Linear Systems
Understand how systems of linear equations arise in machine learning and use matrix methods to solve them.
Vector Spaces
Grasp the concepts of vector spaces, basis, dimension, and linear independence in the context of feature spaces.
Eigenvalues and Eigenvectors
Calculate and interpret eigenvalues and eigenvectors and understand their significance in algorithms like Principal Component Analysis (PCA).
Matrix Decompositions
Apply matrix decompositions like Singular Value Decomposition (SVD) for tasks such as dimensionality reduction.
Implementation Skills
Translate linear algebra concepts into practical Python code using libraries like NumPy and SciPy.