Open Source Python Education Software
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Python Education Software
View 4158 business solutionsBrowse free open source Python Education Software and projects below. Use the toggles on the left to filter open source Python Education Software by OS, license, language, programming language, and project status.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Zero Install is a decentralised cross-distribution software installation system. Create one package that works everywhere! With dependency handling and automatic updates, full support for shared libraries, and integration with native package managers
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Brain Workshop
Python implementation of the Dual N-Back mental exercise
Brain Workshop is a Python implementation of the Dual N-Back mental exercise. This exercise is the only mental activity that has been scientifically shown to improve your short-term memory (working memory) and fluid intelligence. -
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Mnemosyne resembles a traditional flash-card program but with an important twist: it uses a sophisticated algorithm to schedule the best time for a card to come up for review.
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eXe
eLearning XHTML editor
eXe, the eLearning XHTML editor, is a freely available authoring application that assists teachers in the publishing of web content without the need to become proficient in HTML or XML markup. Resources authored in eXe can be exported to the web or LMS. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Simulation of Urban MObility
SUMO is a microscopic, multi-modal traffic simulation.
SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation. The code and the issue tracker can be found at https://github.com/eclipse-sumo/sumo/ The documentation can be found at https://sumo.dlr.de/docs/ -
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eduActiv8
A collection of interactive educational exercises
eduActiv8 is a free Open Source multi-platform educational application that aims to assist in learning various early education topics - from learning the alphabet and new words, colours, time to a wide range of maths-related subjects. eduActiv8 is a continuation of the development of the pySioGame project just under a new name. It is being developed on GitHub at: https://github.com/imiolek-ireneusz/eduActiv8 but 'compiled' releases are published here. The latest version has been partially redesigned to improve usability, but certain activities will still be in the somewhat "prototype quality" - this will be gradually redesigned as time allows. Currently, it is available for Windows, MacOS, Linux and Android (you may need to allow unknown sources to install it on Android). Packages for multiple Linux distributions are available from: https://software.opensuse.org//download.html?project=home%3Aimiolek-i&package=eduactiv8 Support eduActiv8 at https://ko-fi.com/eduactiv8 -
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Educational activities based on multimedia elements (images, sounds, and text). ie. associations, puzzles, counting activities... Creation and modification of the activities using XML files. Multiple language support, multiple screen resolutions (SVG
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GNU Solfege is *free* ear training software written in Python 3.4 using the Gtk+ 3 toolkit. The program is designed to be easily extended with lesson files (data files), so the user can create new exercises.
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GCompris : I got IT
Educational Software for children aged 2 to 10.
GCompris is a high quality educational software suite comprising of numerous activities for children aged 2 to 10. Some of the activities are game oriented, but nonetheless still educational. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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If you are looking for the childsplay application please go to http://www.childsplay.mobi
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Advanced Trigonometry Calculator
Open-source C/C++ math engine for advanced scientific computing
Advanced Trigonometry Calculator (ATC) is an open-source mathematical computing engine written mainly in C/C++. Created in 2011 and maintained by Renato Alexandre dos Santos Freitas, ATC is designed as a practical Windows desktop application for advanced calculations, automation, and technical problem solving. ATC supports equation solving, polynomial tools, complex numbers, matrix calculations, statistics, physics, geometry, unit conversions, DSP/FFT operations, and scripting. It can also process batch TXT files, making it useful for repeatable calculations and automated workflows. The project also includes ATC Online Alpha, a WebAssembly-based version that brings parts of the ATC engine to the web. ATC is released under the GPLv3 license and is made in Portugal. -
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Ajenti 2
Ajenti Core and stock plugins
Ajenti is a Linux & BSD modular server admin panel. Ajenti 2 provides a new interface and a better architecture, developed with Python3 and AngularJS. Ajenti 2 can be easily installed with pip and the provided script. Picks up your current configuration and works on your existing system as-is, without any preparation. Does not overwrite your config files, options and comments. All changes are non-destructive. Includes lots of plugins for system and software configuration, monitoring and management. Ajenti 2 is easily extensible using Python. Plugin development is quick and pleasant with Ajenti APIs. Write your first plugin. Pleasant to look at, satisfying to click and accessible anywhere from tablets and mobile. Small memory footprint and CPU usage. Runs on low-end machines, wall plugs, routers and so on. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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Digital Forensics Guide
Learn all about Digital Forensics and Computer Forensics
The Digital Forensics Guide repository is a comprehensive, structured reference for investigators, analysts, students, and cybersecurity professionals interested in digital forensics principles, tools, methodologies, and workflows. It organizes foundational topics such as evidence acquisition, disk and memory analysis, file system structures, network forensics, artifact extraction, timeline generation, and reporting into digestible modules that help build core competency. Alongside conceptual explanations, the guide includes practical examples with widely used tools (like Autopsy, Volatility, Sleuth Kit, and network analysis suites), illustrating how investigations proceed from initial data capture to final analysis. The goal is to provide both a learning path and a quick reference for real-world casework, bridging the gap between academic theory and operational practice. -
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nbgrader
A system for assigning and grading notebooks
nbgrader is a tool that facilitates creating and grading assignments in the Jupyter notebook. It allows instructors to easily create notebook-based assignments that include both coding exercises and written free responses. nbgrader then also provides a streamlined interface for quickly grading completed assignments. -
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Dotbot
A tool that bootstraps your dotfiles
Dotbot is a lightweight tool for bootstrapping and installing dotfiles on new or existing machines. It helps users keep configuration files in version control while automatically linking them into the locations where applications expect to find them. The project is designed to be self-contained, dependency-light, and easy to run from a dotfiles repository. Its configuration can be written in YAML or JSON, making setups readable and repeatable. Dotbot can create folders, clean broken symbolic links, run shell commands, and manage file or folder links. It is especially useful for developers who want an idempotent dotfiles installer that can be safely rerun whenever they sync updates across systems. -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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Mastering Bitcoin
Mastering Bitcoin 3rd Edition - Programming the Open Blockchain
The bitcoinbook repository contains the source code for Mastering Bitcoin, the authoritative open-source book by Andreas M. Antonopoulos on Bitcoin and cryptocurrency technologies. Written in a collaborative and continuously updated format using Markdown and AsciiDoc, the book serves as a comprehensive technical guide for developers, engineers, and system architects who want to understand how Bitcoin works. It covers the protocol, cryptography, peer-to-peer architecture, wallets, mining, and application development. -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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Ansible Automation Platform Workshops
Training course for Ansible automation platform
The Red Hat Ansible Automation Workshops project is intended for effectively demonstrating Ansible's capabilities through instructor-led workshops or self-paced exercises. These interactive learning scenarios provide you with a pre-configured Ansible Automation Platform environment to experiment, learn, and see how the platform can help you solve real-world problems. The environment runs entirely in your browser, enabling you to learn more about our technology at your pace and time. The demos are intended for effectively demonstrating Ansible capabilities with prescriptive guides on the Ansible Automation Workshop infrastructure. Check out the optional website which is rendered automatically from markdown files using Github Pages. -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production may be older. As any other linter, it is opinionated. Still, its rules are the result of community contributions and they can always be disabled based individually or by category by each user. ansible-lint checks playbooks for practices and behavior that could potentially be improved. As a community-backed project ansible-lint supports only the last two major versions of Ansible. -
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Archivematica
Free and open-source digital preservation system
Archivematica is a web- and standards-based, open-source application which allows your institution to preserve long-term access to trustworthy, authentic, and reliable digital content. Our target users are archivists, librarians, and anyone working to preserve digital objects. You are free to copy, modify, and distribute Archivematica with attribution under the terms of the AGPLv3 license. Archivematica is an open-source application based on recognized standards that makes it possible to preserve long-term access to your institution's digital content. Archivematica is a set of free software tools that allow the user to process digital objects from the moment they are entered into the system until their publication according to the ISO-OAIS functional model. The user can monitor and control the ingestion and preservation of micro-services through the control panel. Archivematica uses standards such as METS, PREMIS, Dublin Core, and the BagIt specification. -
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Claude How-To
A visual, example-driven guide to Claude Code
Claude How-To is a visual, example-driven guide for learning Claude Code. It covers basic concepts, memory, slash commands, hooks, skills, subagents, MCP configuration, plugins, and advanced agent workflows. The project is designed as a practical learning path rather than a simple list of notes. It includes copy-paste templates that users can apply directly to their own projects. The guide also uses diagrams and structured examples to explain not only how features work, but why they matter in a real development workflow. Its main value is helping developers move from casual Claude Code usage to more organized, automated, and agent-driven software development. -
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Concordia
Crowdsourcing platform for full text transcription and tagging
Concordia is a platform for crowdsourcing transcription and tagging of text in digitized images. It was developed by the Library of Congress so that volunteers of all backgrounds could transcribe and tag digitized images of manuscripts and typed materials from the Library’s collections that could not otherwise be done by optical character recognition.
