Shypd.aiThe largest open index of AI toolsMachine Learning Basics provides plain Python implementations of fundamental machine learning algorithms, designed to offer a basic understanding of their underlying structure without relying on external libraries.
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Machine Learning Basics is a GitHub repository offering straightforward Python implementations of core machine learning algorithms. All algorithms are built from scratch, avoiding additional machine learning libraries, to help users grasp the fundamental concepts and internal workings of these algorithms. The collection includes implementations for Bayesian Linear Regression, various decision trees, k-nearest-neighbor, k-Means clustering, Linear and Logistic Regression, Perceptron, Principal Component Analysis, simple neural networks, Softmax regression, and Support Vector Machines. It also features notebooks for data preprocessing, including image preprocessing, aiming to provide a basic understanding of these essential steps.
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