Dyakonov/ml_hacks
Приёмы в машинном обучении
This project offers practical examples and demonstrations for those learning about machine learning and data analysis. It provides hands-on illustrations of core concepts like clustering, anomaly detection, and algorithm parameter tuning. Students, educators, and self-learners in data science or related fields would find these materials useful for understanding how various machine learning techniques work.
202 stars. No commits in the last 6 months.
Use this if you are studying machine learning and need clear, practical examples to understand fundamental algorithms and techniques.
Not ideal if you are looking for a ready-to-use library for a production application or advanced, specialized machine learning research.
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202
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75
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Jupyter Notebook
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Last pushed
Feb 25, 2024
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