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A comprehensive collection of Python code, notes, and resources exploring the mathematical foundations essential for data science and artificial intelligence. This repo covers linear algebra, calculus, probability, statistics, and optimization, providing practical implementations and theoretical insights.

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Mathematical Foundations for Data Science and AI

This repository contains course materials, Python code implementations, and personal notes for the "Mathematical Foundations for Data Science and Artificial Intelligence" class.

Contents

  • Python implementations of key algorithms and concepts
  • Lecture notes and summaries
  • Practice problems and solutions
  • Resources and references

Topics covered include:

  • Linear Algebra
  • Calculus
  • Probability and Statistics
  • Optimization
  • Information Theory

This repository serves as a personal learning archive and a potential resource for others studying the mathematical underpinnings of data science and AI.

Feel free to explore, and if you find any errors or have suggestions, please open an issue!

Note: This is a personal educational repository. All content is either original or properly attributed to its source.

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A comprehensive collection of Python code, notes, and resources exploring the mathematical foundations essential for data science and artificial intelligence. This repo covers linear algebra, calculus, probability, statistics, and optimization, providing practical implementations and theoretical insights.

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