Welcome to my Daily Machine Learning Challenge; a personal commitment to sharpen my machine learning skills by tackling one concept, algorithm, or problem every day.

• Build intuition through daily practice
• Cover both classic ML and deep learning topics
• Implement models from scratch and using libraries like scikit-learn, TensorFlow, and PyTorch
• Explore real-world datasets and tasks
• Learn something new every single day

• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines
• K-Means Clustering
• PCA
• Neural Networks
• Convolutional Neural Networks (CNNs)
• Autoencoders
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)

Each day is organized into a separate folder:

daily-ml/
├── day01-linear-regression/
│ ├── notebook.ipynb
│ └── README.md
├── day02-logistic-regression/
│ ├── ...
...

Each folder includes:

• A Jupyter notebook or script
• A brief explanation of the concept
• Key takeaways and notes

• Python 3.x
• Jupyter Notebooks
• Scikit-learn
• PyTorch / TensorFlow (as needed)
• Matplotlib / Seaborn for visualization

Follow along by starring 🌟 or watching the repo — and feel free to fork it and start your own daily ML journey!