Welcome to my Machine Learning repository! This collection is a comprehensive guide to key Machine Learning concepts, techniques, and practical implementations. I've organized the content into modules, each focusing on different aspects of Machine Learning, from foundational principles to advanced algorithms and projects.
deep-learning tensorflow numpy scikit-learn keras pandas seaborn matplotlib convolutional-neural-networks support-vector-machine machine-learning-basics torchvision decision-tree-implementation data-preprocessing-techniques linear-regression-projects apriori-algorithm-evaluation logistic-regression-projects naive-bayes-knn-projects neural-networks-implementation overfitting-underfitting-clustering
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Updated
Feb 11, 2026 - Jupyter Notebook