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Mathematics for Machine Learning Specialization

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Price: 5.00 USD | Size: 3.50 GB | Duration : 9.30+  Hours

BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD

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Price: 5.00 USD | Size: 3.50 GB | Duration : 9.30+  Hours

BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD

 

Mathematics for Machine Learning Specialization

Content

1.  Mathematics for Machine Learning Linear Algebra

01. Introduction Solving data science challenges with mathematics

02. Motivations for linear algebra

03. Getting a handle on vectors

04. Operations with vectors

05. Summary

06. Introduction to module 2 – Vectors

07. Modulus & inner product

08. Cosine & dot product

09. Projection

10. Changing basis

11. Basis, vector space, and linear independence

12. Applications of changing basis

13. Summary

14. Matrices, vectors, and solving simultaneous equation problems

15. How matrices transform space

16. Types of matrix transformation

17. Composition or combination of matrix transformations

18. Solving the apples and bananas problem Gaussian elimination

19. Going from Gaussian elimination to finding the inverse matrix

20. Determinants and inverses

21. Summary

22. Introduction Einstein summation convention and the symmetry of the dot product

23. Matrices changing basis

24. Doing a transformation in a changed basis

25. Orthogonal matrices

26. The Gram–Schmidt process

27. Example Reflecting in a plane

28. Welcome to module 5

29. What are eigenvalues and eigenvectors

30. Special eigen-cases

31. Calculating eigenvectors

32. Changing to the eigenbasis

33. Eigenbasis example

34. Introduction to PageRank

35. Summary

36. Wrap up of this linear algebra course

 

2. Mathematics for Machine Learning Multivariate Calculus

01. Welcome to Multivariate Calculus

02. Welcome to Module 1!

03. Functions

04. Rise Over Run

05. Definition of a derivative

06. Differentiation examples & special cases

07. Product rule

08. Chain rule

09. Taming a beast

10. See you next module!

1. Welcome to Module 2!

2. Variables, constants & context

3. Differentiate with respect to anything

4. The Jacobian

5. Jacobian applied

6. The Sandpit

7. The Hessian

8. Reality is hard

9. See you next module!

1. Welcome to Module 3!

2. Multivariate chain rule

3. More multivariate chain rule

4. Simple neural networks

5. More simple neural networks

6. See you next module!

1. Welcome to Module 4!

2. Building approximate functions

3. Power series

4. Power series derivation

5. Power series details

6. Examples

7. Linearisation

8. Multivariate Taylor

9. See you next module!

1. Welcome to Module 5!

2. Gradient Descent

3. Constrained optimisation

4. See you next module!

1. Simple linear regression

2. General non linear least squares

3. Doing least squares regression analysis in practice

4. Wrap up of this course

 

3. Mathematics for Machine Learning PCA

1. Introduction to the course

2. Welcome to module 1

3. Mean of a dataset

4. Variance of one-dimensional datasets

5. Variance of higher-dimensional datasets

6. Effect on the mean

7. Effect on the (co)variance

8. See you next module!

1. Welcome to module 2

2. Dot product

3. Inner product definition

4. Inner product length of vectors

5. Inner product distances between vectors

6. Inner product angles and orthogonality

7. Inner products of functions and random variables (optional)

8. Heading for the next module!

1. Welcome to module 3

2.  Projection onto 1D subspaces

3. Example projection onto 1D subspaces

4. Projections onto higher-dimensional subspaces

5. Example projection onto a 2D subspace

6. This was module 3!

1. Welcome to module 4

10. This was the course on PCA

2. Problem setting and PCA objective

3. Finding the coordinates of the projected data

4. Reformulation of the objective

5. Finding the basis vectors that span the principal subspace

6. Steps of PCA

7. PCA in high dimensions

8. Other interpretations of PCA (optional)

9. Summary of this module

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