Welcome to my ML Code Challenges repository! This project is dedicated to tackling daily ML and deep learning challenges offered by Deep-ML, a platform that provides a range of tasks to strengthen our skills and understanding of machine learning, deep learning, and data science fundamentals.
This is an ongoing project where I will continuously add new solutions to challenges, exploring a wide range of ML concepts, from linear algebra fundamentals to advanced neural network implementations.
The ML Code Challenges aim to:
- Enhance practical ML skills by working on real-world inspired tasks.
- Deepen understanding of core machine learning and deep learning concepts.
- Encourage consistent learning with daily challenges covering various difficulty levels, from Easy to Hard.
Each challenge solution is organized in Jupyter notebooks with code explanations, step-by-step solutions, and documentation for easy navigation and learning.
Below are the completed challenges so far. This list will grow as I continue to tackle new tasks.
- Solve Linear Equations using Jacobi Method (medium) Code, Link
- Principal Component Analysis (PCA) Implementation (medium) Code, Link
- Calculate Eigenvalues of a Matrix (medium) Code, Link
- Calculate Covariance Matrix (medium) Code, Link
- Calculate 2x2 Matrix Inverse (medium) Code, Link
A big thanks to Deep-ML for providing these excellent daily challenges, and to the open-source community for inspiring collaborative learning in ML!