Mathematical Foundations of Machine Learning
About Course
Learn the mathematical foundations of machine learning for free with this comprehensive course from Udemy, taught by expert Dr. Jon Krohn. This course dives deep into essential concepts like linear algebra, calculus, and tensor operations, helping you understand the algorithms behind powerful machine learning models.
This course covers:
 Linear Algebra Data Structures
 Tensor Operations
 Matrix Properties
 Eigenvectors and Eigenvalues
 Matrix Operations for Machine Learning
 Limits
 Derivatives and Differentiation
 Automatic Differentiation
 PartialDerivative Calculus
 Integral Calculus
This course goes beyond basic libraries like Scikitlearn and Keras, providing a thorough understanding of the math that powers them. Learn through handson exercises, Python code demos, and practical assignments.
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Course Content
Data Structures of Linear Algebra

What Linear Algebra Is
23:29 
Plotting a System of Linear Equations
09:18 
Linear Algebra Exercise
05:06 
Tensors
02:33 
Scalars
13:04 
Vectors and Vector Transposition
12:19 
Norms and Unit Vectors
14:37 
Basis
04:30 
Matrix Tensors
08:23 
Generic Tensor Notation
06:43 
Exercises on Algebra Data Structures
02:07 
Course Material Download Link
00:00
Tensor Operations
Matrix Properties
Eigenvectors and Eigenvalues
Matrix Operations for Machine Learning
Limits
Derivatives and Differentiations
Automatic Differentiation
Partial Derivative Calculus
Integral Calculus
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