This repository provides a clear and concise overview of fundamental machine learning theories and concepts. It is designed to help beginners understand the mathematical and conceptual foundations behind popular machine learning algorithms.
- Key machine learning concepts and definitions
- Mathematical explanations of algorithms like Linear Regression, Logistic Regression, and Decision Trees
- Theory behind supervised and unsupervised learning
- Bias-variance tradeoff and model evaluation principles
- Resources for further reading and study
Basic knowledge of calculus, linear algebra, and probability will be helpful.
Study the theory notes and examples provided to strengthen your understanding of machine learning principles before diving into coding.
Contributions are welcome! Feel free to open issues or submit pull requests to improve the content.
This project is licensed under the MIT License
Happy learning and exploring machine learning theory!