Linear Algebra for GATE DA | Complete Course

1a. Why Study Linear Algebra | The Importance of Linear Algebra in Machine Learning | Sachin Mittal

1b. Linear Algebra for GATE and in IIT Interviews | MTech Research Interviews | Sachin Mittal

1c. Linearly Independent and Linearly Dependent in Linear Algebra | GO Classes | Sachin Mittal

2a. Filling the space | Linear Algebra | Sachin Mittal | GO Classes

2b. Filling the Space Part-2 | Linear Algebra | Sachin Mittal | GO Classes

2c Summary So far | Linear Algebra | Sachin Mittal | GO Classes

2d. A New Approach to Multiply a Matrix and a Vector | Linear Algebra | Sachin Mittal | GO Classes

3a Why Solving System Of Linear Equations| Linear Algebra | Sachin Mittal | GO Classes

3b Writing as Ax=b, Geometric Interpretation and Possible Solutions | Linear Algebra| Sachin Mittal

3c. Understanding Ax=b intuitively | Many Intuitive questions | Linear Algebra | Sachin Mittal

4a Multiplying 2 Matrices | My Walmart Interview Question | Linear Algebra | Sachin Mittal

4b. GATE 2016 Question | Linear Algebra | GATE CS Preparation | GO Classes | Sachin Mittal

4c GATE 2014 Question | Rank One Matrix | Linear Algebra | GO Classes | Sachin Mittal

4d. Two More Conceptual Questions | Flow Chart for Solutions of Ax = b | Linear Algebra | Sachin Sir

4e. Linear Combination of Linearly Independent Vectors is Unique| Linear Algebra | Sachin Mittal

4f. GATE 2017 Question | Linear Algebra | GO Classes | Sachin Mittal

5a. Echelon Form and Pivot Columns | Linear Algebra | Sachin Mittal | GO Classes

5b. Gaussian Elimination | Linear Algebra | Sachin Mittal

5c. Rank Of a Matrix | GATE 1994 Question | Rank Nullity Theorem | Linear Algebra | Sachin Mittal

5d Finally Solving Ax=b | Five Different Questions | Linear Algebra | Sachin Mittal

5e. Understanding Parametric form of Solution of Ax = b | Infinite Solutions | Linear Algebra

6a GATE 2021 Question | Free Variables in Non Homogeneous | Linear Algebra

6b. Number Of Solutions of Ax=b Based on Rank (A) and Rank (A|b) | Linear Algebra | Sachin Mittal

6c Few Questions Related to Rank (A) and Number of Solutions | Sachin Mittal | Linear Algebra

6d [OPTIONAL] How Elementary "ROW" Operations Lead to Linearly Independent "Columns"? | GO Classes

6e. [Optional] Row Reduced Echelon Form of a Matrix | Linear Algebra | Sachin Mittal

Lecture 7a. Determinant of a Matrix | Linear Algebra | Sachin Mittal | GO Classes

Lecture 7B Inverse of a Matrix | Linear Algebra | Sachin Mittal | GO Classes

Lecture 7C Cramer's Rule | Linear Algebra | Sachin Mittal | GO Classes

Lecture 8a. Introduction to Eigenvalues and Eigenvectors | Geometric Interpretations |Linear Algebra

Lecture 8b. x is EigenVector then kx is also EigenVector | Characteristic Equation | Linear Algebra

Lecture 8c. Questions on Solving for Eigenvalues and Eigenvectors | One Important Theorem | Sachin

Lecture 9a. Linearly Independent Eigen Vectors with Repeating Eigenvalues | Interesting Examples

Lecture 9b. Three Different Examples With Repeating Lambda | GO Classes | Sachin Mittal

Lecture 9c. Symmetric Matrices has n LI Eigen Vectors | Symmetric Matrices Are Blessing in Disguise

Lecture 9d. Eigenvalues and Determinants Got Some Tricks Up Their Sleeve | Linear Algebra | Sachin

10a. Do We Have Any Relation Between Rank and Eigen Values of a Matrix ? | Linear Algebra | Sachin

10b. Questions on Relationship Between Rank and Eigen Values | Linear Algebra | GO Classes | Sachin

10c. Cayley-Hamilton Theorem | Compute Powers of a Matrix Using Its Own Characteristic Polynomial

10d. Relationship Between Eigen Values of AB and BA | A Concept Asked Many Times in Interviews

10e Eigen Values of Powers of a Matrix such as Aᵏ, Aᵏ + 2Aᵏ⁻¹, etc. | Linear Algebra | Sachin

Lecture 10G Matrix Diagonalization in brief | Linear Algebra | Sachin Mittal | GO Classes

Lecture 11a. LU Decomposition | Shortcut to Find Decomposition | Linear Algebra | Sachin Mittal

Lecture 11b. Type Of Matrices | Identity • Triangular matrix • Diagonal • Orthogonal • And Many More

Module 2 Lecture 1 : Introduction to Vector Spaces Subspaces Basis and Span | GO Classes

Lecture 2: Basis, Span, Dimensionality of Vector Space, and Subspaces | Formal Definitions | LA

Lecture 3 Subspaces of Matrix A Column and Null Space

Lecture 4: Row Space and Left Null Space | Four Fundamental Subspaces | Linear Algebra|Sachin Mittal

Lecture 5: Four Fundamental Subspaces | Many Questions | Gilbert Strang Favourite Topic | LA

Lecture 6: Change of Basis | Changing the Basis From Any Arbitrary Basis to Another Basis | GATE DA

Lecture 7: Introduction to Linear Transformation | Treating matrix as Transformation | GATE DA

Lecture 8: More on Linear Transformation | Many Questions | GATE DA | Sachin Mittal | GO Classes LA

Lecture 9: Linear Transformation w.r.t Arbitrary Basis| GATE DA | Sachin Mittal | GO Classes LA

Lecture 10: Many Questions on Linear Transformation and Change of Basis | GATE DA | Sachin Mittal

Lecture 11a: Summary So Far | Vector Spaces and Change of Basis | GATE DA | Sachin Mittal

Lecture 11b: Summary So Far | Linear Transformation | GATE DA | Sachin Mittal

Lecture 12: Similar Matrices and Diagonalization | GO Classes Linear Algebra for GATE DA

Lecture 13: Introduction to Singular Value Decomposition (Part-1) |GO Classes Linear Algebra GATE DA

Lecture 14: Singular Value Decomposition (Part-2) | Singular Values as Eigen Values | GO Classes DA

Lecture 15: Relationship Between Four Fundamental Subspaces and SVD | Linear Algebra |Sachin Mittal

Lecture 16: Geometry of Singular Value Decomposition | SVD | GATE DA | Linear Algebra Sachin Mittal

Lecture 17: 30 Questions on Singular Value Decomposition | SVD | GATE DA | Linear Algebra Sachin Sir

Lecture 18: Orthogonal Projection | GATE DA | Linear Algebra Sachin Mittal

Lecture 19: Projection onto Subspace | GATE DA | Linear Algebra | Sachin Mittal

Lecture 20: Projection Matrix | GATE DA | Linear Algebra | Sachin Mittal

Lecture 21: SVD Quiz Solution | Number of Different SVDs | GATE DA Linear Algebra Module-2 | Sachin

Lecture 22: Introduction to Partition Matrix or Block Matrix | GATE DA Linear Algebra Module-2

Lecture 23: Operations using Partition Matrix or Block Matrix | GATE DA Linear Algebra Module-2

Lecture 24: Positive Definite Matrix And Quadratic Forms of Matrices|GATE DA Linear Algebra Module-2

Geometry of Singular Value Decomposition (SVD) | GO Classes DA

Lecture 1A: Vector Spaces | New Linear Algebra Series for GATE DA

Lecture 1B: Vector Spaces, and Subspace | New Linear Algebra Series for GATE DA

Lecture 2: Basis, Span, Dimensionality of Vector Space, and Subspaces | Formal Definitions | LA

Lecture 3A: Change of Basis | New Linear Algebra Series for GATE DA

Lecture 3B: Linear Transformation | Part 1 | GO Classes

Lecture 3C: Linear Transformation | Part 2 | GO Classes

Lecture 4A: Similar Matrices and Diagonalization | GO Classes

Lecture 4B: Diagonalization | Part 1 | GO Classes

Lecture 4C: Diagonalization | Part 2 | GO Classes