Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Mathematical Foundation For Machine Learning and AI
Introduction
Introduction (3:52)
Linear Algebra
Scalars, Vectors, Matrices, and Tensors (21:14)
Vector and Matrix Norms (9:35)
Vectors, Matrices, and Tensors in Python (21:27)
Special Matrices and Vectors (13:35)
Eigenvalues and Eigenvectors (11:41)
Norms and Eigendecomposition (28:21)
Multivariate Calculus
Introduction to Derivatives (19:24)
Basics of Integration (11:08)
Gradients (12:05)
Gradient Visualization (18:49)
Optimization (18:51)
Probability Theory
Intro to Probability Theory (11:00)
Probability Distributions (10:13)
Expectation, Variance, and Covariance (11:23)
Graphing Probability Distributions in R (12:31)
Covariance Matrices in R (9:49)
Probaility Theory
Special Random Variables (10:52)
Introduction to Derivatives
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock