35 days
Duration
Free
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Tuition fee
Anytime
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Apply date
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About

In this Mathematics for Machine Learning - Linear Algebra course offered by Coursera in partnership with Imperial College London, they look at what linear algebra is and how it relates to vectors and matrices.  

Visit the official programme website for more information

Overview

Key Features

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.

Programme Structure

Courses include:

  • 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

Key information

Duration

  • Part-time
    • 35 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online
  • Self-paced

Academic requirements

We are not aware of any academic requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

  • Beginner Level

Tuition Fee

To alway see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 35 days.
  • National

    Free
    Tuition Fee
    Based 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.

Funding

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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Mathematics for Machine Learning - Linear Algebra
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