Linear Algebra for Data Science and Machine Learning using R

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Syed Mohiuddin

9:45:06

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1. Introduction

1. What you are going to learn in this course.mp4

02:47

2.1 Notes - Introduction.pdf

2. Introduction.mp4

01:47

3.1 Notes - What is Linear Algebra.pdf

3. What is Linear Algebra.mp4

01:10

4.1 Notes - Why Linear Algebra.pdf

4. Why Linear Algebra.mp4

00:56

2. Getting Started with R

1. Installing R Software.mp4

04:34

2. Installing RStudio.mp4

03:12

3. Look around RStudio Interface.mp4

09:07

4. Help & Examples Facility for R Features and Functions.mp4

02:51

5. Changing Look and Feel of RStudio.mp4

01:40

6. Some General Functions Good to Know.mp4

04:38

7. Writing R Program using RGui.mp4

04:14

8. Writing R Program using RStudio.mp4

04:21

9. Using Comments in R Scripts.mp4

01:37

3. Vectors

1.1 Notes - Scalars and Vectors.pdf

1. Scalars and Vectors.mp4

07:42

2. Vectors.html

3.1 Notes - Vectors in 2-Dimensional Space.pdf

3. Vectors in 2-Dimensional Space.mp4

04:32

4. Vectors.html

5.1 Notes - Vectors in 3-Dimensional Space.pdf

5. Vectors in 3-Dimensional Space.mp4

02:41

6.1 Notes - Vectors with n-Components.pdf

6. Vectors with n-Components.mp4

03:20

7.1 creatingvectors.zip

7. R Code - Creating Vectors.mp4

12:35

8. Creating Vectors.html

9. Practice #1.html

10.1 creatingvectorsusingsequences.zip

10. R Code - Create Vectors using Sequence Operator & Function.mp4

09:25

11. Creating Vectors using Sequence.html

12.1 accessingmodifyingvectors.zip

12. R Code - Accessing & Modifying Vectors.mp4

07:24

13. Accessing & Modifying Vectors.html

14.1 Notes - Zero and Ones Vectors.pdf

14. Zero and Ones Vectors.mp4

02:04

15. Zero and Ones Vectors.html

16.1 zeroonesvector.zip

16. R Code - Zero and Ones Vector.mp4

04:20

17. Zero and Ones Vectors.html

18.1 Quiz Solutions - Vectors.pdf

18. Quiz Solutions.html

4. Operations on Vectors

1.1 Notes - Vector Addition.pdf

1. Vector Addition.mp4

04:16

2.1 vectoraddition.zip

2. R Code - Vector Addition.mp4

03:33

3. Vector Addition.html

4.1 Notes - Vector Scalar Multiplication.pdf

4. Scalar Multiplication.mp4

04:55

5.1 scalarmultiplication.zip

5. R Code - Scalar Multiplication.mp4

04:14

6. Scalar Multiplication.html

7.1 Notes - Vector Properties.pdf

7. Vector Properties.mp4

02:45

8. Vector Properties.html

9.1 Notes - Linear Combinations of Vectors.pdf

9. Linear Combinations of Vectors.mp4

02:05

10.1 linearcombination.zip

10. R Code - Linear Combinations of Vectors.mp4

02:00

11. Linear Combination.html

12.1 Notes - Vector Transpose.pdf

12. Vector Transpose.mp4

02:19

13.1 vectortranspose.zip

13. R Code - Vector Transpose.mp4

01:22

14. Vector Transpose.html

15.1 Notes - Dot Product or Inner Product.pdf

15. Dot Product or Inner Product.mp4

05:56

16.1 dotproduct.zip

16. R Code - Dot Product.mp4

06:54

17. Dot Product.html

18.1 Notes - Outer Product.pdf

18. Outer Product.mp4

07:18

19.1 outerproduct.zip

19. R Code - Outer Product.mp4

04:45

20. Outer Product.html

21.1 Quiz Solutions - Operations on Vectors.pdf

21. Quiz Solutions.html

5. Matrices

1.1 Notes - Matrices - Context of Data Science.pdf

1. Matrices - Context of Data Science.mp4

04:10

2. Martrices.html

3.1 Notes - Dimension or Size.pdf

3. Dimension or Size.mp4

04:23

4.1 creatingmatrices.zip

4. R Code - Creating Matrices.mp4

05:53

5.1 matrixfunctions.zip

5. R Code - Matrix Functions.mp4

01:55

6. Matrices.html

7.1 namingrowscolumns.zip

7. R Code - Naming Rows and Columns of Matrix.mp4

03:12

8.1 accessingmodifyingelements.zip

8. R Code - Accessing and Modifying Elements of Matrix.mp4

03:42

9.1 appendingrowscolumns.zip

9. R Code - Appending Rows and Columns.mp4

04:24

10.1 deletingrowscolumns.zip

10. R Code - Deleting Rows and Columns of Matrix.mp4

03:10

11.1 creatingmatrixusingrbindcbind.zip

11. R Code - Creating Matrix using rbind() and cbind().mp4

02:53

12.1 Notes - Matrix Transpose.pdf

12. Matrix Transpose.mp4

04:41

13.1 matrixtranspose.zip

13. R Code - Matrix Transpose.mp4

02:18

14. Matrix Transpose.html

15.1 Notes - Symmetric Matrix.pdf

15. Symmetric Matrix.mp4

04:25

16.1 symmetricmatrix.zip

16. R Code - Symmetric Matrix.mp4

05:23

17. Symmetric Matrices.html

18.1 Notes - Identity Matrices.pdf

18. Identity Matrices.mp4

01:42

19.1 identitymatrices.zip

19. R Code - Identity Matrices.mp4

02:55

20. Identity Matices.html

21.1 Notes - Diagonal Matrix.pdf

21. Diagonal Matrix.mp4

02:00

22.1 diagonalmatrix.zip

22. R Code - Diagonal Matrix.mp4

04:12

23. Diagonal Matrices.html

24.1 Notes - Triangular Matrix.pdf

24. Triangular Matrix.mp4

01:37

25. Triangular Matrix.html

26.1 Notes - Zero and Ones Matrix.pdf

26. Zero and Ones Matrix.mp4

02:04

27.1 zeroonesmatrix.zip

27. R Code - Zero and Ones Matrix.mp4

02:43

28. Zero and Ones Matrix.html

29.1 Quiz Solutions - Matrices.pdf

29. Quiz Solutions.html

6. Operations on Matrices

1.1 Notes - Matrix Addition.pdf

1. Matrix Addition.mp4

05:32

2.1 matrixsums.zip

2. R Code - Matrix Addition.mp4

02:50

3. Matrix Addition.html

4.1 Notes - Scalar Multiplication.pdf

4. Scalar Multiplication.mp4

04:12

5.1 scalarmultiplication.zip

5. R Code - Scalar Multiplication.mp4

02:05

6. Scalar Multiplication.html

7.1 Notes - Hadamard Product.pdf

7. Hadamard Product.mp4

05:38

8.1 hadamardproduct.zip

8. R Code - Hadamard Product.mp4

02:51

9. Hadamard Product.html

10.1 Notes - Trace of Matrix.pdf

10. Trace of Matrix.mp4

02:39

11.1 matrixtrace.zip

11. R Code - Trace of Matrix.mp4

03:03

12. Matrix Trace.html

13.1 Notes - Matrix Multiplication.pdf

13. Matrix Multiplication.mp4

05:21

14.1 matrixmultiplication.zip

14. R Code - Matrix Multiplication.mp4

03:14

15. Matrix Multiplication.html

16.1 Notes - Properties of Matrix Operations.pdf

16. Properties of Matrix Operations.mp4

04:09

17.1 Notes - Matrix Power.pdf

17. Matrix Power.mp4

01:23

18.1 matrixpower.zip

18. R Code - Matrix Power.mp4

04:00

19. Matrix Power.html

20.1 Notes - Diagonal Matrix Multiplication.pdf

20. Diagonal Matrix Multiplication.mp4

06:04

21.1 diagonalmatrixmultiplication.zip

21. R Code - Diagonal Matrix Multiplication.mp4

04:55

22.1 Quiz Solutions - Operations on Matrices.pdf

22. Quiz Solutions.html

7. Matrix Determinant and Inverse

1.1 Notes - Determinants.pdf

1. Determinants.mp4

07:37

2.1 determinants.zip

2. R Code - Determinants.mp4

04:35

3. Determinants.html

4.1 Notes - Properties of Determinants.pdf

4. Properties of Determinants.mp4

01:15

5. Determinants.html

6.1 Notes - Inverse of Matrix.pdf

6. Inverse of Matrix.mp4

04:01

7.1 matrixinverse.zip

7. R Code - Matrix Inverse.mp4

03:18

8. Matrix Inverse.html

9.1 Notes - Singular and Invertible Matrices.pdf

9. Singular and Invertible Matrices.mp4

01:10

10.1 singularmatrices.zip

10. R Code - Singular Matrices.mp4

04:17

11. Singular Matrix.html

12.1 Notes - Properties of Inverse.pdf

12. Properties of Inverse.mp4

01:54

13. Properties of Inverse.html

14.1 Quiz Solutions - Matrix Determinant and Inverse.pdf

14. Quiz Solutions.html

8. Systems of Linear Equations

1.1 Notes - System of Linear Equations.pdf

1. System of Linear Equations.mp4

06:41

2. Systems of Linear Equations.html

3.1 Notes - Types of Systems.pdf

3. Types of Systems.mp4

03:20

4.1 Notes - Matrix Notation.pdf

4. Matrix Notation.mp4

02:55

5. Matrix Notation.html

6.1 Notes - Elementary Row Operations.pdf

6. Elementary Row Operations.mp4

06:40

7.1 Notes - Gauss Elimination Method.pdf

7. Gauss Elimination Method.mp4

09:40

8. Gauss Elimination Method.html

9.1 Notes - Gauss-Jordan Elimination Method.pdf

9. Gauss-Jordan Elimination Method.mp4

07:00

10.1 gaussjordan.zip

10. R Code - Gauss Jordan Elimination Method.mp4

05:00

11. Gauss-Jordan Elimination Method.html

12.1 Quiz Solutions - System of Linear Equations.pdf

12. Quiz Solutions.html

9. Matrix Equations Ax=b

1.1 Notes - Matrix Vector Product Ax=b.pdf

1. Matrix Vector Product Ax=b.mp4

05:25

2. Matrix Vector Product.html

3.1 Notes - Systems of Equations as Linear Combinations.pdf

3. Systems of Equations as Linear Combinations.mp4

04:32

4.1 Notes - Classification of Matrix Vector Product.pdf

4. Classification of Matrix Vector Product.mp4

04:21

5.1 Notes - Solving Systems of Linear Equations using Matrix Inverse.pdf

5. Solving Systems of Linear Equations using Matrix Inverse.mp4

05:23

6.1 solveusinginverse.zip

6. R Code - Solving Systems of Linear Equations using Matrix Inverse.mp4

06:10

7. Solving Systems of Linear Equations using Matrix Inverse.html

8.1 Notes - Solving Systems of Linear Equations using Cramers Rule.pdf

8. Solving Systems of Linear Equations using Cramers Rule.mp4

04:21

9.1 cramersrule.zip

9. R Code - Solving Systems of Linear Equations using Cramers Rule.mp4

06:14

10. Solving Systems of Linear Equations using Cramers Rule.html

11.1 solve.zip

11. R Code - Solving Systems of Linear Equations using qrSolve() function.mp4

06:17

12. Solving Systems of Linear Equations.html

13.1 Quiz Solutions - Matrix Equation Ax=b.pdf

13. Quiz Solutions.html

10. Norms

1.1 Notes - Lengths and Norms.pdf

1. Lengths and Norms.mp4

02:10

2.1 Notes - L2 Norm.pdf

2. L2 Norm.mp4

04:00

3.1 l2norm.zip

3. R Code - L2 Norm.mp4

04:15

4.1 Notes - L1 Norm.pdf

4. L1 Norm.mp4

02:35

5.1 l1norm.zip

5. R Code - L1 Norm.mp4

03:15

6.1 Notes - LP Norm.pdf

6. LP Norm.mp4

02:15

7.1 lpnorm.zip

7. R Code - LP Norm.mp4

02:39

8.1 Notes - L-Infinity Norm.pdf

8. L-Infinity Norm.mp4

02:22

9.1 l-infinitynorm.zip

9. R Code - L Infinity Norm.mp4

02:30

10. Norms.html

11.1 Quiz Solutions - Norms.pdf

11. Quiz Solutions.html

11. Basis Vectors

1.1 Notes - Unit Vectors.pdf

1. Unit Vectors.mp4

04:48

2.1 unitvectors.zip

2. R Code - Unit Vectors.mp4

04:21

3.1 Notes - Standard Basis Vectors and Span.pdf

3. Standard Basis Vectors.mp4

06:09

4.1 Notes - Basis Vectors and Span.pdf

4. Basis Vectors and Span.mp4

07:20

5. Basis Vectors.html

6.1 Quiz Solutions - Basis Vectors.pdf

6. Quiz Solutions.html

12. Linear Independence

1.1 Notes - Linear Independence.pdf

1. Linear Independence - Introduction.mp4

05:07

2. Linear Independence.html

3.1 Notes - Linear Independence based on Basis Vectors.pdf

3. Linear Independence based on Basis Vectors.mp4

02:30

4.1 Notes - Linear Independence based on Determinant or Inverse.pdf

4. Linear Independence based on Determinant or Inverse.mp4

04:30

5. Linear Independence based on Determinant.html

6.1 Quiz Solutions - Linear Independence.pdf

6. Quiz Solutions.html

13. Vector Subspaces

1.1 Notes - Vector Subspaces - Introduction.pdf

1. Vector Subspaces - Introduction.mp4

01:51

2.1 Notes - Null Space.pdf

2. Null Space.mp4

05:35

3.1 nullspace.zip

3. R Code - Null Space.mp4

03:45

4.1 Notes - Null Space and Linear Independence.pdf

4. Null Space and Linear Independence.mp4

05:25

5.1 Notes - Column Space.pdf

5. Column Space.mp4

03:45

6.1 Notes - Rank and Nullity.pdf

6. Rank and Nullity.mp4

04:42

7.1 Notes - Rank-Nullity Theorem.pdf

7. Rank-Nullity Theorem.mp4

02:20

8.1 ranknullity.zip

8. R Code - Rank and Nullity.mp4

04:45

14. Matrix Factorization

1.1 Notes - Factorization.pdf

1. Factorization - Introduction.mp4

00:45

2.1 Notes - LU Factorization.pdf

2. LU Factorization.mp4

01:18

3. LU Factorization.html

4.1 Notes - Computing LU Factorization.pdf

4. Computing LU Factorization.mp4

01:28

5.1 lufactorization.zip

5. R Code - LU Factorization.mp4

04:50

6. LU Factorization.html

7.1 Quiz Solutions - Matrix Factorization.pdf

7. Quiz Solutions.html

15. Orthogonality

1.1 Notes - Orthogonal Vectors.pdf

1. Orthogonal Vectors.mp4

03:00

2.1 orthogonalvectors.zip

2. R Code - Orthogonal Vectors.mp4

02:35

3. Orthogonal Vectors.html

4.1 Notes - Orthonormal Vectors.pdf

4. Orthonormal Vectors.mp4

02:47

5.1 orthonormalvectors.zip

5. R Code - Orthonormal Vectors.mp4

02:34

6. Orthonormal Vectors.html

7.1 Notes - Orthogonal Matrix.pdf

7. Orthogonal Matrix.mp4

01:24

8.1 orthogonalmatrix.zip

8. R Code - Orthogonal Matrix.mp4

05:25

9. Orthogonal Matrix.html

10.1 Quiz Solutions - Orthogonality.pdf

10. Quiz Solutions.html

16. Eigenvalues and Eigenvectors

1.1 Notes - Eigenvalues and Eigenvectors Introduction.pdf

1. Eigenvalues and Eigenvectors - Introduction.mp4

03:50

2. Eigenvalues from Eigenvectors.html

3.1 Notes - Computing Eigenvalues from Eigenvectors.pdf

3. Computing Eigenvalues from Eigenvectors.mp4

04:29

4.1 Notes - Computing Eigenvectors from Eigenvalues.pdf

4. Computing Eigenvectors from Eigenvalues.mp4

03:08

5. Eigenvalues from Eigenvectors.html

6.1 Notes - Characteristic Equation.pdf

6. Characteristic Equation.mp4

02:54

7.1 computingeigenvalueseigenvectors.zip

7. R Code - Computing Eigenvalues and Eigenvectors.mp4

05:51

8. Computing Eigenvalues from Eigenvectors.html

9.1 Notes - Eigen Decomposition.pdf

9. Eigen Decomposition.mp4

05:17

10.1 eigendecomposition.zip

10. R Code - Eigen Decomposition.mp4

05:05

11. Eigen Decomposition.html

12.1 Notes - Diagonalization.pdf

12. Diagonalization.mp4

05:00

13.1 diagonalization.zip

13. R Code - Diagonalization.mp4

03:46

14. Diagonalization.html

15.1 Notes - Few Points - Eigenvalues and Eigenvectors.pdf

15. Few Points - Eigenvalues and Eigenvectors.mp4

01:03

16.1 Quiz Solutions - Eigenvalues and Eigenvectors.pdf

16. Quiz Solutions.html

17. Singlular Value Decomposition

1.1 Notes - Singular Value Decomposition - Introduction.pdf

1. Singular Value Decomposition - Introduction.mp4

02:57

2. Singular Value Decomposition.html

3.1 Notes - Computing Singular Value Decomposition.pdf

3. Steps for Computing SVD.mp4

01:36

4.1 svd.zip

4. R Code - Singular Value Decomposition - SVD.mp4

03:47

5. Singular Value Decomposition.html

6.1 Quiz Solutions - Singular Value Decomposition.pdf

6. Quiz Solutions.html

18. Least Squares Problem

1.1 Notes - Least Squares Problem - Introduction.pdf

1. Least Squares Problem - Introduction.mp4

04:11

2.1 Notes - Least Squares.pdf

2. Least Squares.mp4

08:05

3.1 Notes - Solving Least Squares Problems.pdf

3. Solving Least Squares.mp4

03:12

4.1 leastsquares.zip

4. R Code - Solving Least Squares.mp4

04:18

19. Moore-Penrose Pseudoinverse

1.1 Notes - Moore-Penrose Pseudoinverse.pdf

1. Moore-Penrose Pseudoinverse.mp4

05:52

2.1 moorepenrose.zip

2. R Code - Moore Penrose Pseudoinverse.mp4

04:42

Description

Vectors, Matrices, Solving Linear Equations, Factorization, Eigenvectors, Least Squares, SVD

What You'll Learn?

Fundamentals of Linear Algebra
Applications of Matrices, Vectors and operations on Matrices and Vectors with implementation in R
Solve Systems of Linear Equations and implementation in R
Matrix Factorization and implementation in R
Computation of Eigenvalues, Eigenvectors and Eigen Decomposition with their implementation in R
Solving Least Squares problems
Singular Value Decomposition with its implementation in R

Who is this for?

Anyone who is curious about how Linear Algebra is used in Machine Learning

Anyone who wants to understand Maths and Linear Algebra behind Data Science

Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques

What You Need to Know?

You should have familiarity with fundamentals of Maths

All the implementation of Linear Algebra concepts are in R, so familiarity with R will be an added advantage

More details

Description
This course will help you in understanding of the Linear Algebra and mathâ€™s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.

Thereâ€™re tons of topics in this course. To begin the course:
We have a discussion on what is Linear Algebra and Why we need Linear Algebra
Then we move on to Getting Started with R, where you will learn all about how to setup the R environment, so that itâ€™s easy for you to have a hands-on experience.
Then we get to the essence of this course;
Vectors & Operations on Vectors
Matrices & Operations on Matrices
Determinant and Inverse
Solving Systems of Linear Equations
Norms & Basis Vectors
Linear Independence
Matrix Factorization
Orthogonality
Eigenvalues and Eigenvectors
Singular Value Decomposition (SVD)
Again, in each of these sections you will find R code demos and solved problems apart from the theoretical concepts of Linear Algebra.

You will also learn how to use the R's pracma, matrixcalc library which contains numerous functions for matrix computations and solving Linear Algebric problems.

So, letâ€™s get startedâ€¦.

Who this course is for:
Anyone who is curious about how Linear Algebra is used in Machine Learning
Anyone who wants to understand Maths and Linear Algebra behind Data Science
Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques

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