Search Results for "machine learning projects"
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Showing 1019 open source projects for "machine learning projects"
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
...Contributions are very welcome! General-purpose machine learning and deep learning frameworks. -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. -
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Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
machine-learning-refined is an educational repository designed to help students and practitioners understand machine learning algorithms through intuitive explanations and interactive examples. The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. ... -
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Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. ... -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. ... -
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ML Intern
ML engineer that reads papers, trains models, and ships ML models
ML Intern is a repository by Hugging Face that provides educational content and projects aimed at helping learners gain practical experience in machine learning and AI development. It is designed to simulate the experience of working as a machine learning intern, offering tasks and exercises that mirror real-world workflows. The project includes tutorials, datasets, and example implementations that guide users through different aspects of ML development. ... -
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Machine Learning Engineering Open Book
Machine Learning Engineering Open Book
Machine Learning Engineering Open Book is an open “living book” that captures practical methodologies, tooling advice, and operational knowledge for successfully training and deploying large language models and multimodal systems. The repository functions as a field guide compiled from real-world experience, particularly from work on large-scale models such as BLOOM-176B and IDEFICS-80B. -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
AiLearning-Theory-Applying is a comprehensive educational repository designed to help learners quickly understand artificial intelligence theory and apply it in practical machine learning and deep learning projects. The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. ... -
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machine_learning_examples
A collection of machine learning examples and tutorials
machine_learning_examples is an open-source repository that provides a large collection of machine learning tutorials and practical code examples. The project aims to teach machine learning concepts through hands-on programming rather than purely theoretical explanations. It includes implementations of many machine learning algorithms and neural network architectures using Python and popular libraries such as TensorFlow and NumPy. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
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huggingface_hub
The official Python client for the Huggingface Hub
The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine-learning apps hosted on the Hub. You can also create and share your own models, datasets, and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. -
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Made With ML
Learn how to develop, deploy and iterate on production-grade ML
Made-With-ML is an open-source educational repository and course designed to teach developers how to build production-grade machine learning systems using modern MLOps practices. The project focuses on bridging the gap between experimental machine learning notebooks and real-world software systems that can be deployed, monitored, and maintained at scale. It provides structured lessons and practical code examples that demonstrate how to design machine learning workflows, manage datasets, train models, evaluate performance, and deploy inference services. ... -
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Orion
A machine learning library for detecting anomalies in signals
Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. It also promotes disciplined learning routines and project-based practice so learners can develop practical experience and build deployable solutions. -
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Claude Code Projects Index
An index of my Claude Code related repos
Claude Code Projects Index is a curated directory of projects, tools, and resources built around Claude Code and related AI development ecosystems. It functions as a centralized index that helps developers discover useful repositories, workflows, and integrations. The project is organized to make navigation easy, grouping resources by categories such as tooling, frameworks, and use cases. -
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Google Research: Language
Shared repository for open-sourced projects from the Google AI Lang
Google Research: Language is a shared repository maintained by Google Research that contains open-source projects developed by the Google AI Language team. The repository hosts multiple subprojects related to natural language processing, machine learning, and large-scale language understanding systems. Many of the projects included in the repository correspond to research papers released by Google researchers and provide implementations of new NLP algorithms or experimental frameworks. ... -
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Python Code Tutorials
The Python Code Tutorials
Python Code Tutorials is a large educational repository that aggregates programming tutorials from the “The Python Code” website into a structured collection of Python projects and learning materials. The repository covers a wide range of programming topics including cybersecurity, networking, web scraping, machine learning, GUI development, and automation scripts. Each tutorial typically includes complete Python code examples and explanations that demonstrate how to build real tools and applications step by step. ... -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. -
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100+ Python Projects Challenge
100+ Python Projects Challenge
100+ Python Projects Challenge is a project-based Python learning repository focused on building many small applications and utilities. Instead of only practicing isolated syntax, it encourages learners to apply Python through complete mini-projects. The challenges span practical scripts, beginner applications, automation ideas, games, data tasks, and other hands-on exercises.
