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Mathematical Foundation For Machine Learning and AI
Introduction
Introduction
Linear Algebra
Scalars, Vectors, Matrices, and Tensors
Vector and Matrix Norms
Vectors, Matrices, and Tensors in Python
Special Matrices and Vectors
Eigenvalues and Eigenvectors
Norms and Eigendecomposition
Multivariate Calculus
Introduction to Derivatives
Basics of Integration
Gradients
Gradient Visualization
Optimization
Probability Theory
Intro to Probability Theory
Probability Distributions
Expectation, Variance, and Covariance
Graphing Probability Distributions in R
Covariance Matrices in R
Probaility Theory
Special Random Variables
Expectation, Variance, and Covariance
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