Welcome to the Linear Algebra Fundamentals for Machine Learning repository! This repository is designed to provide a foundational understanding of linear algebra concepts essential for machine learning applications. Through Jupyter Notebooks, we explore vector basics, vector projections, basis vectors, introduction to matrices, and Gaussian elimination. I was motivated by my experience with the LinkedIn Learning course titled "Machine Learning Foundations: Linear Algebra," instructed by Terezija Semenski.
Linear algebra is a fundamental mathematical framework that underlies many machine learning algorithms. In this repository, we focus on key concepts that lay the groundwork for understanding more advanced topics in linear algebra and their applications in machine learning.
Explore the following Jupyter Notebooks in this repository:
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Vectors Basics: Introduction to vectors, their representation, and basic operations.
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Vector Projections and Basis: Understanding vector projections onto other vectors and the concept of a vector basis.
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Introduction to Matrices: Exploring matrices, their representation, and basic operations.
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Gaussian Elimination: Learning the Gaussian elimination method for solving systems of linear equations.
Feel free to delve into these notebooks to grasp the essentials of linear algebra and their direct relevance to machine learning.
To begin your journey with this repository, follow these steps:
Clone the Repository: Clone this repository to your local machine using git clone https://github.com/av1155/Linear-Algebra-in-Machine-Learning.
Install Dependencies: Ensure you have Python and Jupyter Notebook installed. You can install the required packages using pip install jupyter numpy matplotlib.
Access Notebooks: Launch Jupyter Notebook within the repository directory and access the notebooks in your web browser.
To access Jupyter Notebooks online, simply copy the GitHub notebook link and then proceed to paste it at this location: https://nbviewer.org/.
To make the most of this repository, it's helpful to have a basic understanding of:
- Python programming fundamentals.
- Elementary concepts of calculus and algebra.
Utilize these notebooks to:
- Acquire a solid understanding of vector and matrix operations.
- Grasp the significance of vector projections and basis vectors.
- Learn Gaussian elimination as a tool for solving linear systems.
Contributions are encouraged! If you spot errors, wish to correct typos, or intend to enhance the content, please initiate issues or pull requests.
Happy learning! Feel free to reach out through issues or messages if you have queries or suggestions.