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MeTA is a modern C++ data sciences toolkit. It provides text tokenization including deep semantic features like parse trees, inverted and forward indexes with compression and caching strategies, ranking functions for searching indexes, topic models, classification algorithms, graph algorithms, language models, a CRF implementation for POS-tagging and shallow parsing, and wrappers for liblinear and libsvm.
MLU-Explain is an education initiative from Amazon that teaches machine learning concepts through visual essays. It publishes interactive articles on topics such as neural networks, logistic regression, reinforcement learning, ROC and AUC, cross-validation, random forests, and decision trees.
An NLP toolbox for Matlab under development by researchers at UC Berkeley's AMPLab, intended to process natural language within Matlab rather than switching to other languages. It includes modules for text tokenization, text preprocessing such as stop word removal and stemming, and learning algorithms including linear regression, decision trees, support vector machines and a Naive Bayes classifier.
Orange is an open-source machine learning and data visualization toolbox. It offers visual programming in which widgets are placed on a canvas and connected to load datasets and analyze data. It supports interactive visualization methods including box plots, scatter plots, decision trees, hierarchical clustering, heatmaps, MDS, t-SNE and linear projections. Extensions add text mining, network analysis and other specialized capabilities.
Pi is a terminal-based minimal coding agent harness built to adapt to user workflows rather than requiring users to adjust their processes. It supports 15+ providers and hundreds of models, offers tree-structured shareable interaction history, context engineering features, and four operation modes: interactive, print/JSON, RPC, and SDK. Users can customize Pi with extensions, skills, prompt templates, and themes, and share customizations as packages via npm or git. It is installable via curl, PowerShell, npm, pnpm, or bun.
pygal is a Python charting library described as beautiful and simple python charting. Its documentation lists chart types including line, bar, histogram, XY, pie, radar, box, dot, funnel, gauge, pyramid and treemap, and output formats such as SVG, PNG and base 64 data URI.
PyStanfordDependencies is a Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Dependencies. It offers a get_instance() function with subprocess or jpype backends, and convert_tree() or convert_trees() methods returning Token objects. Visualization options include asciitree, Graphviz dotgraphs, and interactive xdot windows. A Stanford CoreNLP or Parser jar file can be supplied, or downloaded automatically.
Rapaio is a statistics, data mining, and machine learning toolbox written in Java. It includes distributions, hypothesis testing, Naive Bayes, logistic regression, decision trees, random forests, boosting, SVM, PCA, and KMeans.
TPOT (Tree-based Pipeline Optimization Tool) is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. The project underwent a major refactoring, merging what was once called TPOT2 into the main package, with new features including genetic feature selection, multi-objective optimization and an expanded method of defining search spaces.
