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Detailed notes and exercises for the "Mathematics for Machine Learning" specialization of Imperial College London.

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Mathematics for Machine Learning 📚🧮

Overview

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

Contents

  • 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

How to Use This Repository

  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! 🚀🧠

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