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.

Linear Algebra	Multivariate Calculus	Principal Component Analysis

• 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

• 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

1. Explore the cheatsheets to get a quick review of topics.
2. 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! 🚀🧠