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Mathematics for Machine Learning and Data Science. Master calculus, linear algebra, statistics & probability - the fundamental math toolkit for machine learning. Course materials, assignments & resources for the beginner-friendly DeepLearning.AI Specialization.

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Mathematics for Machine Learning and Data Science

Welcome to the GitHub repository for the "Mathematics for Machine Learning and Data Science" Specialization, a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is designed to help you master the fundamental mathematical toolkit required for machine learning and data science.

Overview

As a machine learning engineer or data scientist, a strong grasp of mathematics like algorithms (you can do learn these from Khan Academy's Algorithms course) is essential for understanding the underlying principles behind algorithms and data analysis techniques. This Specialization uses innovative pedagogy and visualizations to help you learn these concepts quickly and intuitively, even if you have a limited background in mathematics.

The recommended prerequisites for this program are:

  • At least a high school level understanding of mathematics
  • Basic familiarity with Python programming language

Throughout the Specialization, you'll use Python to demonstrate the learning objectives in the environment where they're most applicable to machine learning and data science.

Contents

This repository contains all the course materials, assignments, and resources for the "Mathematics for Machine Learning and Data Science" Specialization. The contents are organized into the following folders:

  1. Week 1: Systems of linear equations

    • Lecture notes and slides
    • Programming assignments
  2. Week 2: Solving systems of linear equations

    • Lecture notes and slides
    • Programming assignments
  3. Week 3: Vectors and Linear Transformations

    • Lecture notes and slides
    • Programming assignments
  4. Week 4: Determinants and Eigenvectors

    • Lecture notes and slides
    • Programming assignments

Getting Started

To get started with this Specialization, you'll need to have Python installed on your machine. We recommend using the latest version of Python 3 and a popular Integrated Development Environment (IDE) like Jupyter Notebook.

Each course folder contains detailed instructions on how to set up the required environment and complete the assignments. You can also refer to the official DeepLearning.AI course materials for additional guidance and support.

Contributing

While this repository is primarily intended for personal use and learning purposes, we welcome contributions from the community. If you find any errors or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

The contents of this repository are provided under the MIT License. You are free to use, modify, and distribute the materials for personal and non-commercial purposes.

Let's embark on this exciting journey together and unlock the power of mathematics for machine learning and data science!

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Mathematics for Machine Learning and Data Science. Master calculus, linear algebra, statistics & probability - the fundamental math toolkit for machine learning. Course materials, assignments & resources for the beginner-friendly DeepLearning.AI Specialization.

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