📌 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
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