Awesome GitHub RepositoriesRegression Models

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro

This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The

Vowpal Wabbit is an open-source machine learning system designed for online learning, where models update incrementally from streaming data without requiring full retraining. It provides a reduction-based learning framework that composes complex tasks from simpler algorithms, and includes a feature hashing trick that maps unbounded feature names into a fixed-size vector space to keep memory usage constant regardless of dataset size. The system supports distributed training across a cluster using an allreduce protocol for synchronized updates, and offers an active learning query strategy that s

This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes

The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example

यह प्रोजेक्ट शैक्षिक Jupyter Notebooks का एक संग्रह है जो TensorFlow फ्रेमवर्क का उपयोग करके न्यूरल नेटवर्क निर्माण और टेंसर ऑपरेशंस पर ट्यूटोरियल प्रदान करता है। यह मशीन लर्निंग के छात्रों के लिए एक शैक्षिक रिपॉजिटरी और इम्प्लीमेंटेशन गाइड के रूप में कार्य करता है। यह सूट विशिष्ट उन्नत आर्किटेक्चर पर केंद्रित है, जिसमें इमेज क्लासिफिकेशन के लिए कन्वेन्शनल नेटवर्क, ट्रेनिंग स्टेबिलिटी के लिए स्किप कनेक्शन वाले रेजिडुअल नेटवर्क, और जेनरेटिव मॉडलिंग व डेटा सिंथेसिस के लिए वेरिएशनल ऑटोएनकोडर शामिल हैं। इसमें फीचर एक्सट्रैक्शन और डाइमेंशनलिटी रिडक्शन करने के लिए डीनोइजिंग और डीप ऑटोएनकोडर बनाने के लिए गाइड भी शामिल हैं। रिपॉजिटरी प्रेडिक्टिव मॉडलिंग के व्यापक दायरे को कवर करती है, जिसमें निरंतर मानों और बाइनरी परिणामों की भविष्यवाणी के लिए लीनियर, पॉलिनोमियल और लॉजिस्टिक रिग्रेशन के इम्प्लीमेंटेशन शामिल हैं।

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi