8.5 Matrices in Linear Algebra | Types, Operations & Properties | Linear Algebra for ML скачать в хорошем качестве

8.5 Matrices in Linear Algebra | Types, Operations & Properties | Linear Algebra for ML

In this video, we cover Matrices in Linear Algebra - their types, properties, and operations - with clear examples and Machine Learning intuition. Matrices are the backbone of ML models, neural networks, and optimization algorithms, and this session builds a strong foundation required for both ML understanding and interviews. Topics Covered: 1. Introduction to Matrices, Column Vectors, and Row Vectors 2. Important Matrix Concepts Explained with Examples – Square Matrix, Rectangular Matrix, Principal Diagonal, Secondary Diagonal, Upper & Lower Triangular Matrix, Diagonal Matrix, Identity Matrix, Transpose Matrix 3. Advanced Matrix Types Explained with Examples – Idempotent Matrix, Involutary Matrix, Nilpotent Matrix, Symmetric Matrix, Skew-Symmetric Matrix, Orthogonal Matrix 4. Operations on Matrices Explained with Examples – Scalar Multiplication, Matrix Addition, Matrix Multiplication, Trace of a Matrix, Determinant of a Matrix, Inverse of a Matrix, Rank of a Matrix Helpful For: 1. Cracking AI / ML / Data Science interview rounds at top tech companies 2. Building a deeper understanding of core AI, ML concepts 3. Preparing for GATE (DA / CS / Other streams) and other related competitive exams Our Playlist: Linear Algebra for ML - Hindi:    • 8. Linear Algebra for ML   #LinearAlgebra #Matrices #MachineLearningMath #MathForML #MatrixOperations #MLFoundations #AI #DataScience #DeepLearning #MLInterviews #decodeaiml Tags : matrices linear algebra, matrix types, matrix operations, square matrix, rectangular matrix, diagonal matrix, identity matrix, transpose matrix, symmetric matrix, skew symmetric matrix, orthogonal matrix, idempotent matrix, involutary matrix, nilpotent matrix, determinant of matrix, inverse of matrix, rank of matrix, trace of matrix, matrix multiplication, calculus for ml, linear algebra for machine learning, ml interview math