Udemy - Linear Algebra for Machine Learning - AI with no math pre...

Linear Algebra for Machine Learning/AI with no math prereqs

https://WebToolTip.com

Published 8/2025
Created by Rebecca Tyler
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 19 Lectures ( 4h 24m ) | Size: 2.54 GB

Jupyter Notebooks, Python, and LaTex will be utilized. No math prerequisites!

What you'll learn
Learners will render correct mathematical symbology using LaTeX (the mathematical standard), which includes using a LaTeX editor.
Learners will utilize Python’s sympy library to perform symbolic mathematical calculations.
Learners will distinguish between scalars, vectors, matrices, and tensors.
Learners will perform linear algebra operations mentally, by hand, and using Python (sympy).
Learners will interpret 2D and 3D graphs in terms of matrices and and/or vectors.
Learners will analyze various methods for utilizing Python matrices in terms of time, memory, and efficiency.
Given an rref matrix, learners will calculate the rank and column space of the matrix.
Learners will apply linear algebra knowledge to make decisions about feature importance/feature selection.
Learners will recognize and construct linear transformations including, rotating, reflecting, and scaling.
Learners will apply the inverse of a transformation, when possible, to get back to the original dataset.
Learners will graphically convey the relationship between eigenvalues, eigenvectors, and their original transformation matrix.
Given an eigenvector, learners will construct an eigenspace.
Given eigenvalues and eigenvectors, learners will perform an eigendecomposition of a matrix.

Requirements
No math prerequisites. No programming prerequisites.
Access to Python or Colab. All code is provided; however, you should have a general working understanding of Python.