Machine Learning Algorithms
Showing 26 of 26 projects
A 12-week, 26-lesson curriculum teaching classic machine learning using Scikit-learn through hands-on projects and quizzes.
A Python ETL framework for stream processing, real-time analytics, and building live LLM/RAG pipelines, powered by a scalable Rust engine.
A top-down, hands-on daily study plan for software engineers transitioning into machine learning roles.
Python implementations of popular machine learning algorithms from scratch with interactive Jupyter demos and mathematical explanations.
Matlab implementation of machine learning algorithms from Bishop's Pattern Recognition and Machine Learning textbook.
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
A comprehensive collection of tutorials, examples, and resources for understanding and solving machine learning and pattern classification problems.
A Python machine learning toolkit for time series analysis with scikit-learn compatible API.
A Python library for machine learning on graphs and networks, offering state-of-the-art algorithms for tasks like node classification and link prediction.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A free software AI accelerator that speeds up scikit-learn applications by 10-100x on CPUs and GPUs with no code changes.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A fast, ergonomic machine learning library for Rust with broad algorithm coverage and WASM-first defaults.
A pure Java machine learning library with no external dependencies, offering a wide collection of algorithms and parallel execution support.
A Ruby library for text classification with Bayesian, LSI, logistic regression, k-NN, and TF-IDF algorithms.
A high-performance C++/DPC++ library for accelerated machine learning on CPUs, GPUs, and distributed systems.
A Python framework for gradient-free optimization, featuring common algorithms like genetic algorithms and simulated annealing.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A Python library for interpretable text classification using the SS3 model, with built-in visualization tools for explainable AI.
A parallel Monte Carlo and machine learning library for scientific inference, available in Python, MATLAB, Fortran, C++, and C.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A Scala and JVM machine learning toolbox for research, education, and industry with an interactive REPL and end-to-end pipelines.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
A Python library implementing fairness-aware machine learning algorithms for measuring and mitigating discrimination in predictive models.
Saul is a declarative domain-specific language in Scala for designing flexible machine learning models with relational feature extraction.
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