Linear Algebra Fundamentals for Machine Learning

• Represent Data with Vectors and Matrices

  Understand how to represent data points, features, and entire datasets using vectors and matrices.

• Perform Core Linear Algebra Operations

  Execute fundamental operations such as vector addition, dot products, matrix multiplication, and transposition.

• Interpret Geometric Transformations

  Visualize how matrices act as functions to rotate, scale, and shear vector spaces.

• Solve Systems of Linear Equations

  Set up and solve systems of linear equations, a common task in optimization and model fitting.

• Understand Eigenvalues and Eigenvectors

  Grasp the concepts of eigenvalues and eigenvectors and their significance in algorithms like Principal Component Analysis (PCA).

• Implement Concepts in Python

  Use the NumPy library in Python to perform linear algebra computations efficiently.

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