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https://github.com/kientech/mathematics-for-machine-learning-specialization
Mathematics for Machine Learning Specialization
https://github.com/kientech/mathematics-for-machine-learning-specialization
mathematics pca python statistics
Last synced: about 1 month ago
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Mathematics for Machine Learning Specialization
- Host: GitHub
- URL: https://github.com/kientech/mathematics-for-machine-learning-specialization
- Owner: kientech
- Created: 2022-12-22T05:55:18.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-12-22T09:26:08.000Z (about 2 years ago)
- Last Synced: 2024-04-21T02:55:02.332Z (9 months ago)
- Topics: mathematics, pca, python, statistics
- Homepage:
- Size: 9.77 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Mathematics for Machine Learning Specialization
## π Summary
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how itβs used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science..In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.
At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.
## π Main Topics
- Implement mathematical concepts using real-world data.
- Derive PCA from a projection perspective.
- Understand how orthogonal projections work.
- Master PCA.## π Key Skills Learned
- Eigenvalues And Eigenvectors.
- Principal Component Analysis (PCA).
- Machine Learning.
- Multivariable Calculus.
- Linear Algebra.
- Basis (Linear Algebra).
- Transformation Matrix.
- Linear Regression.
- Vector Calculus.
- Gradient Descent.
- Dimensionality Reduction.
- Python Programming.## π Certificates
To verify the certificates, click the images to follow the links.
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