This repository contains detailed notes from the course "Mathematics for Machine Learning Specialization" offered by Imperial College London (link). This course series is designed to provide a solid foundation of mathematical knowledge needed for applications in data science and machine learning.
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| Linear Algebra | Multivariate Calculus | Principal Component Analysis |
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Cheatsheets: Useful cheatsheets on the key topics of the course, which can be used as a review of the main concepts:
- Linear Algebra
- Multivariate Calculus
- Principal Component Analysis
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Python Labs: A series of Jupyter notebooks that correspond to the labs and exercises done throughout the course. They provide a hands-on opportunity to apply the mathematical knowledge acquired, with projects that include:
- PageRank algorithm for an Internet simulation
- Training Neural Networks using multivariate computation
- Nonlinear regression to fit models to datasets
- Feature analysis of the MNIST dataset through Principal Component Analysis
- Explore the cheatsheets to get a quick review of topics.
- Run and experiment with Jupyter notebooks to apply knowledge through hands-on projects.
Have fun exploring and refreshing your mathematical knowledge for machine learning and data science! 🚀🧠