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Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying solely on black-box frameworks. This makes the repo suitable for students, hobbyists, or developers who want to deeply understand how ML algorithms work under the hood and experiment with parameter tuning or custom data. Because it's part of the author’s learning-path repositories, it likely is integrated with tutorials, sample datasets, and contextual guidance, which helps users bridge theory.
Features
- Implementations of classic machine learning algorithms in Python: classification and regression models like kNN, decision trees, SVM, logistic regression, etc.
- Simple and readable code — ideal for learners to understand algorithm internals and flow without complex abstractions
- Sample datasets and example usage so one can run experiments, test performance, and observe behavior directly
- Good for educational use, prototyping, or baseline implementations before migrating to more advanced frameworks
- Enables custom modification or extension — you can tweak algorithm parameters, add new data preprocessing, or integrate with pipelines as needed
- Open-source and widely used among learners, increasing chance of community contributions, bug fixes, or extensions over time
Project Samples
