Description
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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