This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
This course is part of the Mathematics for Machine Learning Specialization
About this Course
What you will learn
Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Skills you will gain
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Syllabus - What you will learn from this course
Statistics of Datasets
Principal Component Analysis
- 5 stars51.28%
- 4 stars22.60%
- 3 stars12.73%
- 2 stars6.62%
- 1 star6.75%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.
Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).
The course is generally good but the assignment setting definitely needs to be rectified. Thanks anyway for this course. An important element of machine learning.
Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!
About the Mathematics for Machine Learning Specialization
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