Line Introduction || Lecture 01 || Linear Algebra

Slope, Plane and Hyperplane || Line || Lecture 1.2

Euclidean and Manhattan Distances || Line || Lecture 1.3

Introduction to VECTORS || LINEAR ALGEBRA || Lecture 1.4

DOT PRODUCT OF VECTORS || Vector|| Linear Algebra || Lecture 1.5

Matrix Introduction || Matrix || Linear Algebra || Lecture 1.6

Algebraic Operations || Matrix || Linear Algebra || Lecture 1.7

Inverse and Transpose of Matrix || Matrix || Linear Algebra || Lecture 1.8

System of General Linear Equations || Matrix || Linear Algebra || Lecture 1.9

Introduction to Machine Learning || Hindi || Lecture 1.10

Types Of Machine Learning || Hindi || Lecture 1.11

Batch Learning v/s Online Learning || ML || Lecture 1.12

Full Introduction of Linear Regression || ML || Lecture 1.13

Linear Regression || Part 2 || Lecture 1.14

Gradient Descent and Learning Rate in Linear Regression || Machine Learning || Basics

Linear Regression in Multi Variables || ML Algorithm || Lecture 1.16

Is GRADIENT DESCENT working Correctly? || Linear Regression || Lecture 1.17

Implementation of Linear Regression || Using Formula|| Lecture 1.18

Linear Regression Implementation on Real Dataset || Machine Learning-Ep.02 || Lecture 1.19

Linear Regression Implementation using Scikit Learn || Machine Learning-Ep.03 || Lecture 1.20

Logistic Regression || Introduction || Machine Learning || Lecture 1.21

Best Introduction to Machine Learning and Maths Concept | Class - 1 | ML Free

Linear Algebra of Linear Regression | Class - 2 | ML Free

Pearson Coefficient, Variance, R Value in Linear Regression | Class - 3 | ML Free

Errors, Coeff. of Determinant and Gradient Descent in Linear Regression | Class - 4 | ML Free

Normal v/s Standard Normal Distribution in Machine learning | ML in Hindi

Best Ever Roadmap to start ML/AI | 2025