Linear Algebra for Machine Learning and Data Science

📌 Linear Algebra | Complete Tutorial for Machine Learning & Data Science 📌 In this tutorial, we cover the fundamental concepts of Linear Algebra that are essential for machine learning, deep learning, and data science. Whether you're a beginner or looking for a refresher, this video will help you understand key topics like matrices, eigenvalues, eigenvectors, and dimensionality reduction techniques like PCA (Principal Component Analysis). 0:00 Introduction to Linear Algebra 11:20 System of Equations 1:18:08 Solving Systems of Linear Equations - Elimination 1:38:11 Solving Systems of Linear Equations - Row Echelon Form and Rank 2:06:26 Vector Algebra 2:33:14 Linear Transformations 3:00:07 Determinants In-depth 3:18:02 Eigenvalues and Eigenvectors Resources link - https://github.com/Ryota-Kawamura/Mathematics-for-Machine-Learning-and-Data-Science-Specialization 🔹 Topics Covered in This Tutorial: ✅ System of Equations – Learn how to set up and solve systems of linear equations ✅ Solving Systems of Linear Equations – Step-by-step guide using elimination and row echelon form ✅ Vector Algebra – Understanding vectors, vector spaces, and transformations ✅ Linear Transformations – How transformations work in machine learning models ✅ Determinants & Eigenvalues/Eigenvectors – Core concepts for dimensionality reduction ✅ Matrix Operations – Essential matrix manipulations for deep learning algorithms ✅ PCA (Principal Component Analysis) – How it helps with dimensionality reduction in data science #LinearAlgebra #MachineLearning #DataScience #DeepLearning #MathForML #PCA #DimensionalityReduction #Eigenvalues #Eigenvectors #DataScienceBootcamp #MathForDataScience