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
Offered By
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
Master PCA
Skills you will gain
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Offered by
Syllabus - What you will learn from this course
Statistics of Datasets
Inner Products
Orthogonal Projections
Principal Component Analysis
Reviews
- 5 stars51.12%
- 4 stars22.59%
- 3 stars12.78%
- 2 stars6.68%
- 1 star6.81%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: PCA
Course content is interesting and well planned, Can be improved by making it Simpler for Students as it was more technical than the other 2 courses of the Specialization.
This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!
This course is well worth the time. I have a better understanding of one of the most foundational and biologically plausible machine learning algorithms used today! Love it.
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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