rushter/MLAlgorithms

• zotroneneis/machine_learning_basics

  zotroneneis/machine_learning_basics

  4,418عرض على GitHub↗

  This project is a collection of foundational machine learning algorithms and tools implemented from scratch in Python. It serves as a library of core implementations for regression, classification, and clustering models, designed to demonstrate the underlying mathematical structures of these algorithms without relying on high-level machine learning frameworks. The project focuses on the manual implementation of algorithmic logic, including neural networks with forward propagation and weight updates, as well as various supervised and unsupervised learning models. It utilizes NumPy for vectoriz

  Jupyter Notebookalgorithmipynbk-nearest-neighbor

  عرض على GitHub↗4,418
• dod-o/statistical-learning-method_code

  Dod-o/Statistical-Learning-Method_Code

  11,621عرض على GitHub↗

  This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations. It serves as an educational resource for studying the mathematical foundations and inner workings of machine learning models through manual implementations. The codebase provides hand-coded implementations of both supervised and unsupervised learning. This includes classification and regression models such as support vector machines, decision trees, and Naive Bayes, as well as data clustering and pattern discovery methods like k-means and hierarchi

  Pythoncodemachine-learning-algorithmsstatistical-learning-method

  عرض على GitHub↗11,621
• assemblyai-community/machine-learning-from-scratch

  AssemblyAI-Community/Machine-Learning-From-Scratch

  971عرض على GitHub↗

  Machine-Learning-From-Scratch is an educational repository that provides implementations of fundamental machine learning models built using standard Python programming logic. It serves as a resource for understanding the internal mechanics of common statistical and predictive algorithms by constructing them from the ground up rather than relying on high-level machine learning frameworks. The project distinguishes itself by prioritizing transparency in algorithmic design, utilizing mathematical primitives and vectorized array computations to expose the underlying calculus and statistical logic

  Python

  عرض على GitHub↗971
• kaieye/2022-machine-learning-specialization

  kaieye/2022-Machine-Learning-Specialization

  4,603عرض على GitHub↗

  This repository is a collection of machine learning course materials, providing study notes and Python implementation examples for a professional specialization. It serves as a guide for supervised and unsupervised learning, focusing on the application of fundamental algorithms. The content covers a broad range of machine learning education, including the mathematical foundations and practical prototyping of models. It specifically provides resources for implementing regression, classification, clustering, and dimensionality reduction techniques. The project is organized as a curriculum-base

  Jupyter Notebook

  عرض على GitHub↗4,603

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