This repository contains Jupyter notebooks that transcribe exercises and knowledge from various mathematics books related to machine learning. The primary goal is to translate theoretical concepts into practical code examples, making it easier for learners to understand and apply these concepts in their machine learning projects.
- Books
- Basics of Linear Algebra for Machine Learning by Jason Brownlee
- Mathematics to Machine Learning
- Project Structure
- Installation
- Usage
- Contributing
- License
This book covers the fundamental aspects of linear algebra that are crucial for understanding and implementing machine learning algorithms. The topics include:
- Vectors
- Vector Norm
- Matrix
- Types of Matrix
- Matrix Operations
- Transpose
- And more...
This book provides a comprehensive guide to the mathematical foundations required for machine learning. It covers various topics that bridge the gap between theoretical mathematics and practical machine learning applications.
The repository is organized into directories, each corresponding to a book. Each directory contains Jupyter notebooks for the chapters covered in the respective book.