The virtual course "Mathematics for machine learning: PCA - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 18 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
This intermediate level course introduces the mathematical underpinnings for deriving principal component analysis (PCA), a fundamental dimensionality reduction technique.
We'll cover some basic statistics of data sets, such as mean values and variances, calculate distances and angles between vectors using inner products, and derive orthogonal projections of data into lower-dimensional subspaces.
Using all of these tools, we will then derive PCA as a method that minimizes the mean squared reconstruction error between the data points and their reconstruction.
By the end of this course, you will be familiar with important mathematical concepts and be able to implement PCA on your own.
If you're struggling, you've got I'll find a set of jupyter notebooks that will let you explore the properties of techniques and walk you through what you need to do to get up and running.
If you are already an expert, this course can refresh some of your knowledge.
The lectures, examples and exercises require: 1.
Some capacity for abstract thinking 2.
Good background in linear algebra (p.
ex.
, matrix and vector algebra, linear independence, basis) 3.
Basic training in multivariate calculus (p.
ex.
, partial derivatives, basic optimization ) 4.
Basic knowledge of python and numpy programming Disclaimer: This course is substantially more abstract and requires more programming than the other two courses in the specialization.
However, this kind of abstract thinking, algebraic manipulation, and programming is necessary if you want to understand and develop machine learning algorithms.
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Imperial College London
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