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Showing 1644 open source projects for "learn"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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Learn Elixir
Learn the Elixir programming language to build functional web apps
learn-elixir is a practical, beginner-friendly guide to learning Elixir and the Erlang VM (BEAM), emphasizing why Elixir scales—lightweight processes, immutable data, robust GC, and supervisors—and how to apply those strengths in real projects. The repo walks you from installation on macOS, Ubuntu, and Windows to an interactive workflow using iex, Livebook, and even a one-line Docker run for a zero-install setup. -
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Ai-Learn
The artificial intelligence learning roadmap compiles 200 cases
Ai-Learn is an open-source artificial intelligence learning roadmap that aggregates educational materials, tutorials, and practical projects designed to help beginners study AI and machine learning systematically. The repository was created to help learners start self-study programs in artificial intelligence without getting overwhelmed by the large number of available resources. -
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Learn Go
Master the fundamentals and advanced features of the Go language
Learn Go is a learning-focused repository built to teach Go from first principles through more advanced language and ecosystem features. It takes a course-like approach, starting with environment setup and the “Hello world” basics, then moving into variables, types, formatting, control flow, and functions. From there, it expands into Go’s practical structure for real projects, including modules, packages, and workspaces, plus useful commands for building and working with programs. ... -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in ML
Imbalanced-learn (imported as imblearn) is an open source, MIT-licensed library relying on scikit-learn (imported as sklearn) and provides tools when dealing with classification with imbalanced classes. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
Learn Claude Code is an educational repository that teaches how modern AI coding agents work by walking learners through a sequence of progressively more complex agent implementations, starting with a minimal Bash-based agent and culminating in agents with explicit planning, subagents, and skills. It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. ... -
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Learn Git Branching
An interactive git visualization and tutorial
LearnGitBranching (LGB) is a Git repository visualizer, sandbox, and interactive tutorial platform that teaches Git concepts through visualization and gamified challenges. Instead of only typing commands into the terminal, users see a live commit tree update dynamically as they experiment with branching, merging, rebasing, and more. It features both sandbox mode for free exploration and structured levels to guide learners through Git fundamentals and advanced workflows. Designed entirely... -
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Learn Go with Tests
Learn Go with test-driven development
Katas are fun but they are usually limited in their scope for learning a language; you're unlikely to use goroutines to solve a kata. Another problem is when you have varying levels of enthusiasm. Some people just learn way more of the language than others and when demonstrating what they have done end up confusing people with features the others are not familiar with. This ends up making the learning feel quite unstructured and ad hoc. By far the most effective way was by slowly introducing the fundamentals of the language by reading through go by example, exploring them with examples and discussing them as a group. ... -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Microsoft Learn MCP Server
Official Microsoft Learn MCP Server, powering LLMs and AI agents
Microsoft Learn MCP Server is the official GitHub repository for the Microsoft Learn MCP (Model Context Protocol) Server, a service that implements the Model Context Protocol to provide AI assistants and tools with reliable, real-time access to Microsoft’s official documentation. Rather than relying on training data that may be outdated or incomplete, MCP servers let agents like GitHub Copilot, Claude, or other LLM-based tools search and pull context directly from up-to-date Microsoft Learn content, including Azure, .NET, and other tech docs. ... -
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Learn Regex The Easy Way
Learn regex the easy way
Learn Regex The Easy Way is a hands-on, interactive resource for learning regular expressions (regex) in a step-by-step, incremental way. Rather than just being a reference sheet, it is designed to help you build understanding gradually: you start with the basics like literal matching, then advance through character classes, quantifiers, groups, alternation, lookaheads/lookbehinds, and more advanced regex features. -
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Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
Learn PyTorch for Deep Learning is an open-source educational repository that provides the full learning materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course created by Daniel Bourke. The project is designed to teach beginners how to build deep learning models using PyTorch through a hands-on, code-first learning approach. -
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The Arcade Learning Environment
The Arcade Learning Environment (ALE) -- a platform for AI research
Arcade Learning Environment (ALE) is a widely used open-source framework that wraps hundreds of Atari 2600 games via an emulator and presents them as RL environments for AI agents. It decouples the game/emulation aspects from the agent interface, providing a clean API (C++, Python, Gymnasium) so researchers can focus on agent design rather than game plumbing. This environment suite has been central to many RL breakthroughs, including value-based agents, deep Q-nets, and general-agent... -
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
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Roadmap To Learn Generative AI In 2025
Basic Machine Learning Natural Language Processing Roadmap
Roadmap To Learn Generative AI In 2025 is a curated learning path focused on contemporary generative AI — covering large language models (LLMs), diffusion-based image generation, prompt engineering, multi-modal AI, fine-tuning techniques, and the practical considerations for deploying generative models. It’s aimed at learners and developers who already have some programming or ML basics and wish to specialize in generative AI, offering a modern, structured plan that reflects the state of the art as of 2025. ... -
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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions... -
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GDevelop
The game engine for everyone
GDevelop is an open-source, cross-platform game development software designed to make creating HTML5 and native games easy for everyone. Experts and novices alike can create their own games with GDevelop as it is fast and easy to learn, requiring no knowledge in a specific programming language. What makes GDevelop unique and so easy to use is its powerful and intuitive event-based system. Events are a powerful way to build up your game logic, without having to learn a programming language. GDevelop also features ready-made behaviors for your game objects, and allows you to create new behaviors through the easy to learn events. ... -
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Apollo Client
Use GraphQL to build client applications
GraphQL client that will allow you to easily use data from GraphQL with any JavaScript frontend. -
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Tidal
Pattern language
...Whether you're using SuperDirt or a synth, every filter and effect can be manipulated independently with Tidal patterns. Tidal is embedded in the Haskell language, although you don't have to learn Haskell to learn Tidal. You can learn Tidal through experimentation and play, most Tidal coders have little or no experience in software engineering. -
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gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. ... -
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scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
scikit-learn-videos repository accompanies a video tutorial series designed to teach machine learning using Python’s scikit-learn library. It provides the Jupyter notebooks used in each lesson so learners can reproduce the demonstrations and experiment with the code themselves. The series introduces fundamental machine learning concepts such as classification, regression, model evaluation, feature engineering, and cross-validation using clear examples and real datasets. ... -
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FastAPI Python
FastAPI framework, high performance, easy to learn, fast to code
FastAPI framework, high performance, easy to learn, fast to code, ready for production. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. -
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skfolio
Python library for portfolio optimization built on top of scikit-learn
skfolio is a Python library designed for portfolio optimization and financial risk management that integrates closely with the scikit-learn ecosystem. The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. ... -
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Lightning Web Components
A Blazing Fast, Enterprise-Grade Web Components Foundation
...Leave abstractions behind and build custom elements with HTML, JavaScript, and CSS. Lightning web components are custom elements built using HTML and modern JavaScript. Ready to learn more? Check out the Developer Guide and write code in the Playground. You’ll be up and running in no time. Create fast, versatile web components and apps using the stack and tools your team prefers. Shape the framework through your pull requests and help push native Web Components forward. Use the latest web standards to build robust components that run in modern browsers and IE 11. ... -
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SKORCH
A scikit-learn compatible neural network library that wraps PyTorch
A scikit-learn compatible neural network library that wraps PyTorch.
