Mathematics for Machine Learning: Linear Algebra
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Mathematics for Machine Learning: Linear Algebra at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Mathematics for Machine Learning: Linear Algebra at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 3 in the Mathematics for Machine Learning Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 19 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Mathematics for Machine Learning: Linear Algebra at Coursera Course details
- In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
- Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you?ve not coded before.
- At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
Mathematics for Machine Learning: Linear Algebra at Coursera Curriculum
Introduction to Linear Algebra and to Mathematics for Machine Learning
Introduction: Solving data science challenges with mathematics
Motivations for linear algebra
Getting a handle on vectors
Operations with vectors
Summary
About Imperial College & the team
How to be successful in this course
Grading policy
Additional readings & helpful references
Exploring parameter space
Solving some simultaneous equations
Doing some vector operations
Vectors are objects that move around space
Introduction to module 2 - Vectors
Modulus & inner product
Cosine & dot product
Projection
Changing basis
Basis, vector space, and linear independence
Applications of changing basis
Summary
Dot product of vectors
Changing basis
Linear dependency of a set of vectors
Vector operations assessment
Matrices in Linear Algebra: Objects that operate on Vectors
Matrices, vectors, and solving simultaneous equation problems
How matrices transform space
Types of matrix transformation
Composition or combination of matrix transformations
Solving the apples and bananas problem: Gaussian elimination
Going from Gaussian elimination to finding the inverse matrix
Determinants and inverses
Summary
Using matrices to make transformations
Solving linear equations using the inverse matrix
Matrices make linear mappings
Introduction: Einstein summation convention and the symmetry of the dot product
Matrices changing basis
Doing a transformation in a changed basis
Orthogonal matrices
The Gram?Schmidt process
Example: Reflecting in a plane
Non-square matrix multiplication
Example: Using non-square matrices to do a projection
Eigenvalues and Eigenvectors: Application to Data Problems
Welcome to module 5
What are eigenvalues and eigenvectors?
Special eigen-cases
Calculating eigenvectors
Changing to the eigenbasis
Eigenbasis example
Introduction to PageRank
Summary
Wrap up of this linear algebra course
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Selecting eigenvectors by inspection
Characteristic polynomials, eigenvalues and eigenvectors
Diagonalisation and applications
Eigenvalues and eigenvectors
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