Implementing Linear Algebra Operations in Python

Welcome to the Gudsky Applied AI & ML Educational Series!

In this hands-on session we implement the core linear algebra operations that power machine learning — using Python + NumPy. This practical video is perfect for beginners who want to move from theory to code and see how vectors and matrices behave in real programs.

🔍 What you’ll learn (practical demos):
Creating vectors and matrices with np.array()
Vector operations: addition, subtraction, scalar multiply
Dot product, magnitude (norm), and normalization of vectors
Matrix operations: transpose, matrix multiplication (@ / np.matmul)
Matrix inverse (np.linalg.inv) and determinant (np.linalg.det)
Useful linear algebra utilities: np.linalg.solve, np.linalg.eig (intro)
Small practical examples: linear transformation (2D rotation/scaling) and a student exercise (grades × weights → final score)

🔁 Follow along
Copy the code from the video into a Colab/Jupyter notebook and run each cell. Try the hands-on exercise at the end to solidify learning.

📘 Next up
Next video: Statistical Analysis of Real Dataset

🔔 If you found this useful, Like, Subscribe, and turn on notifications to follow the full 6-month Applied AI & ML Course by Gudsky Research Foundation.

#LinearAlgebra #NumPy #PythonForML #VectorMath #MatrixOperations #MathForML #ImplementingLinearAlgebra #DataScienceBasics #MachineLearningMath #NumPyTutorial #PythonTutorial #AIandML #AppliedAI #CodingForBeginners #MathInAI #LinearTransformation #DotProduct #MatrixMultiplication #MatrixInverse #Eigenvalues #NormalizeVectors #VectorOperations #JupyterNotebook #GoogleColab #GudskyAI