Open-source alternatives to Libsvm

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

Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

This project serves as an educational and practical resource for mastering machine learning workflows using Python. It provides a comprehensive collection of code examples and exercises designed to guide users through the implementation of predictive systems, ranging from fundamental algorithms to deep learning architectures. The repository distinguishes itself by offering a structured approach to both classical machine learning and neural network training. It covers the full lifecycle of model development, including the orchestration of reusable data transformation pipelines, advanced ensemb

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

This project provides a translated version of the scikit-learn machine learning library guides and API references for Chinese speakers. It serves as a localized knowledge base and technical reference for implementing predictive data analysis and statistical modeling using a Python-based toolkit. The resource covers the implementation of supervised learning, including classification and regression tasks, and unsupervised learning workflows for pattern discovery and anomaly detection. It also provides guidance on data science education, specifically focusing on the use of scikit-learn for machi

Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit, and a network analysis tool. It provides a mathematical framework for categorizing datasets through a vector space model library. The project enables the extraction of structured data from web services and the creation of searchable web content indexes. It processes unstructured text using sentiment analysis, part-of-speech tagging, and n-gram searching. The library covers machine learning classification through the training of models using perceptron algorithms and support ve

Ludwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data. The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener

This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

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 is a Python machine learning library featuring a collection of core algorithms implemented from scratch to demonstrate foundational AI concepts. It provides a comprehensive toolkit for supervised learning, unsupervised learning, and neural network development. The project is distinguished by its custom implementation of a neural network framework, which includes multi-layer perceptrons with backpropagation, gradient descent, and weight regularization. It also includes a specialized anomaly detection toolkit that identifies outliers and rare events using Gaussian probability distributions

NNI is an AutoML toolkit designed to automate machine learning lifecycles. It functions as a hyperparameter optimization framework, a neural architecture search tool, and a model compression suite. The project provides a distributed training orchestrator to manage machine learning workloads across local machines, remote servers, and cloud platforms. It enables the discovery of efficient model structures through reinforcement learning and one-shot optimization methods, while utilizing Bayesian and evolutionary algorithms to automate hyperparameter tuning. Additional capabilities include tools

cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data preprocessing and model execution. It provides a suite of tools for training and deploying classification, regression, and clustering models on NVIDIA GPUs and GPU clusters. The library is designed for scalability, offering a distributed GPU machine learning environment that can spread computation and data across multiple hardware accelerators and nodes to handle datasets exceeding single-device memory. It mirrors standard estimator interfaces to allow the replacement of CPU-based models

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

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

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

Linfa is a classical machine learning framework and statistical learning suite implemented in Rust. It provides a collection of algorithms for supervised and unsupervised learning, focused on traditional statistical methods such as regression, clustering, and decision trees. The toolkit is distinguished by its ability to be compiled into WebAssembly, enabling analytical models to execute within browser environments. It employs a trait-based algorithm interface to standardize the process of training and prediction across its various models. The library covers a broad range of capabilities, in

Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning

This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

This project is a machine learning algorithm reference and implementation guide that provides theoretical foundations and code for supervised learning, deep learning, and natural language processing. It serves as a comprehensive toolkit for implementing predictive models and a technical reference for algorithm engineering. The project focuses on ensemble learning frameworks, including the construction of decision trees, random forests, and gradient boosting models. It also functions as a probabilistic graphical model library and an NLP algorithm reference, with specific implementations for se

This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G

This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders

Skorch is a deep learning workflow manager and tensor-based model interface. It provides a consistent API for training and predicting with neural networks within standard machine learning workflows, acting as a hyperparameter optimizer for finding optimal network configurations. The library specializes in wrapping PyTorch neural networks in a scikit-learn compatible interface. This allows tensor-based models to be used within traditional machine learning pipelines and grid search tools, including the mapping of parameter grids to model configurations. The framework covers training lifecycle

This repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle. The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning. The examples cover a broad range of capabilities i

This project provides a collection of practical machine learning code examples, including implementations for supervised, unsupervised, and reinforcement learning algorithms. It features deep learning model implementations for convolutional, recurrent, and generative architectures, alongside specific examples of reinforcement learning agents that maximize rewards in simulated environments. The repository includes dedicated data preprocessing pipelines for sanitization, feature scaling, and dimensionality reduction. It also provides implementations for a wide range of specific models, such as