Prerequisites: Basic Math and Python

What You'll Learn

• 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.