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 Mathematics for Machine Learning - Linear Algebra course offered by Coursera in partnership with Imperial College London, 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.
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- Linear Algebra and to Mathematics for Machine Learning
- Vectors are objects that move around space
- Matrices in Linear Algebra: Objects that operate on Vectors
- Matrices make linear mappings
- Eigenvalues and Eigenvectors: Application to Data Problems
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- 35 days
Start dates & application deadlines
DisciplinesMathematics Informatics & Information Technology Machine Learning View 336 other Short Courses in Informatics & Information Technology in United States
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We are not aware of any academic requirements for this programme.
We are not aware of any English requirements for this programme.
- Beginner Level
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InternationalFreeTuition FeeBased on the tuition of 0 USD for the full programme during 35 days.
NationalFreeTuition FeeBased on the tuition of 0 USD for the full programme during 35 days.
You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access.
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.
Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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Apply and win up to €10000 to cover your tuition fees.