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View All on GitHubml-core is a community-driven, Python-based machine learning repository structured into specialized domains like computer vision, NLP, time series, and more. It serves as a curated hub for projects, notebooks, and model implementations — ideal for learning, collaboration, and contribution.
AI Summary: Implement a perceptron algorithm for binary classification in Python or C++. The perceptron should update weights using the perceptron learning rule and be tested on a small, linearly separable dataset. The implementation should include well-structured code and a README explaining the approach and results.
ml-core is a community-driven, Python-based machine learning repository structured into specialized domains like computer vision, NLP, time series, and more. It serves as a curated hub for projects, notebooks, and model implementations — ideal for learning, collaboration, and contribution.
AI Summary: Implement a K-means clustering algorithm in Python or C++, testing it on a small 2D dataset with 3 clusters and 10 points. The implementation should include centroid initialization, point assignment, and centroid updates until convergence. The code should be well-structured and include a README explaining the approach and showing sample results.
ml-core is a community-driven, Python-based machine learning repository structured into specialized domains like computer vision, NLP, time series, and more. It serves as a curated hub for projects, notebooks, and model implementations — ideal for learning, collaboration, and contribution.
AI Summary: Implement a Gaussian Naive Bayes classifier in Python or C++, including training (calculating class means and variances) and prediction (using probability densities). The implementation should be tested on a small dataset with two classes and two features. This task is part of a larger open-source machine learning project.
ml-core is a community-driven, Python-based machine learning repository structured into specialized domains like computer vision, NLP, time series, and more. It serves as a curated hub for projects, notebooks, and model implementations — ideal for learning, collaboration, and contribution.
AI Summary: Implement a linear regression model using gradient descent in Python, trained on a small dataset (10 points, 2 features), and capable of making predictions. The implementation should include functions for weight computation and prediction, and be documented with a README.
ml-core is a community-driven, Python-based machine learning repository structured into specialized domains like computer vision, NLP, time series, and more. It serves as a curated hub for projects, notebooks, and model implementations — ideal for learning, collaboration, and contribution.