Mathematics for Machine Learning
Mathematics for Machine Learning ranks 5th in the list of best online Linear Algebra courses. In this course on Linear Algebra you look at what direct algebra is and how it relates to vectors and matrices also they 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 break problems. Eventually you look at how to use these to do delightful effects with datasets- like how to rotate images of faces and how to prize eigenvectors to look at how the Pagerank algorithm works.
Since they are aiming at data- driven operations, they'll be enforcing some of these ideas in law, not just on pencil and paper. Towards the end of the course, you will write law blocks and encounter Jupyter scrapbooks in Python, but do not worry, these will be relatively short, riveted on the generalities, and will guide you through if you ’ve not enciphered ahead. At the end of this course you'll have an intuitive understanding of vectors and matrices that will help you bridge the gap into direct algebra problems, and how to apply these generalities to machine literacy.
Syllabus of the Course-
- Systems of linear equations and linear classifier
- Full rank decomposition and systems of linear equations
- Euclidean spaces
- Final Project
Extra Benefits-
- You will get a Shareable Certificate upon completion.
- Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.
Who Should Enroll?
- Those who are familiar with Python Programming and basic algebra.
Time to Complete- 14 hours
Rating: 4.6/5.0
Enroll here: coursera.org/specializations/mathematics-machine-learning
