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Machine learning basics repository is an educational project that provides plain Python implementations of fundamental machine learning algorithms designed to help learners understand how these methods work internally. Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets.
Features
- Pure Python implementations of core machine learning algorithms
- Jupyter notebooks explaining algorithm structure and theory
- Coverage of regression, classification, clustering, and dimensionality reduction
- Examples including decision trees, SVMs, perceptrons, and neural networks
- Data preprocessing tutorials for different dataset types
- Interactive notebooks for experimentation and algorithm study
Project Samples
