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machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis. The repository integrates numerous popular machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. It aims to strike a balance between theoretical explanation and practical coding by demonstrating algorithms both from scratch and using established libraries. The content is organized into multiple sections covering topics such as clustering, regression, dimensionality reduction, recommender systems, and model evaluation.
Features
- Educational notebooks demonstrating machine learning algorithms in Python
- Coverage of topics such as deep learning, reinforcement learning, and time series
- Implementations using libraries like scikit-learn, PyTorch, and TensorFlow
- Balanced explanations combining mathematical intuition and code
- Practical tutorials covering model deployment and evaluation techniques
- Continuously updated documentation of data science experiments and projects
Project Samples
