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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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coursera.org
Andrew Ng’s Machine Learning at Stanford University is a Coursera course titled 'Supervised Machine Learning: Regression and Classification'. It is the first course of the Machine Learning Specialization. The course teaches how to build machine learning models in Python using NumPy and scikit-learn, covering regression and classification. It is aimed at beginners, requires flexible scheduling over three weeks at ten hours per week, and offers a shareable certificate.
github.com
Machine Learning Module is a GitHub repository that claims to host the best machine learning tutorials on the web. It invites contributions from the community and serves as a collection of learning resources for machine learning.
rentry.org
A staged learning roadmap for machine learning that covers mathematics and calculus, programming, and machine learning concepts. It advises moving to the next stage only after fully understanding the current one. The mathematics section covers linear algebra, calculus, and probability and statistics, and the programming section covers Python basics and advanced Python.
github.com
Awesome Machine Learning with Ruby is a GitHub-hosted curated list of resources for machine learning development using the Ruby programming language. It includes tutorials and machine learning libraries for Ruby users.
github.com
Machine Learning in MatLab/Octave is a collection of MatLab/Octave implementations of popular machine learning algorithms, including code examples and explanations of the associated mathematics. It covers supervised learning tasks such as regression and classification as well as unsupervised learning.
github.com
Machine Learning for Software Engineers is a study resource that provides a complete daily study plan for software engineers seeking to become machine learning engineers, structured as a top-down learning path.
machinelearningmastery.com
Machine Learning Mastery is a website that provides educational content such as tutorials, quick start guides, and eBooks to help developers learn and apply machine learning concepts and algorithms. It covers topics like deep learning, computer vision, natural language processing, and specific libraries including TensorFlow, PyTorch, and scikit-learn.
Approaching (Almost) Any Machine Learning Problem is a GitHub repository or organization with source code, releases, documentation, or project.
astroml.org
astroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib and astropy, distributed under the 3-clause BSD license. It provides statistical and machine learning routines for analyzing astronomical data, loaders for open astronomical datasets, and examples of analyzing and visualizing datasets. The project was started in 2012 to accompany a textbook on statistics, data mining and machine learning in astronomy.
github.com
Datumbox is an open-source machine learning framework written in Java for rapid development of machine learning and statistical applications. It focuses on including many machine learning algorithms and statistical methods and on handling large datasets. The code is licensed under the Apache License, Version 2.0, and releases are available on Maven Central, including version 0.8.2.
github.com
Repository containing references and code for the book Distributed Machine Learning Patterns from Manning Publications by Yuan Tang. The book covers applying patterns to build scalable and reliable machine learning systems, constructing pipelines with data ingestion, distributed training and model serving, and automating tasks with Kubernetes, TensorFlow, Kubeflow and Argo Workflows.
icml.cc
ICML is an international machine learning conference supported by the International Machine Learning Society. The 32nd International Conference on Machine Learning, ICML 2015, was held in Lille, France, on July 6 to July 11, 2015, with tutorials, a main conference and workshops.
music.youtube.com
Machine Learning – Software Engineering Daily provides machine-learning models, research, training resources, or evaluation tools.
github.com
Machine Learning Visualized is a GitHub repository or organization with source code, releases, documentation, or project resources.
github.com
Machine Learning Zoomcamp is a free, hands-on course on building, evaluating, and deploying machine learning systems. It covers the path from framing a problem to preparing data, training and evaluating models, and deploying them, using Python, scikit-learn, TensorFlow, PyTorch, FastAPI, Docker, Kubernetes and AWS Lambda. No prior machine learning experience is required.
github.com
A Matlab package implementing machine learning algorithms described in the textbook Pattern Recognition and Machine Learning by C. Bishop. It is written purely in Matlab and is self-contained with no external dependency.
github.com
Pipcook is a JavaScript application framework for machine learning and its engineering, aimed at Web developers. It provides subprojects including a machine learning pipeline framework, management tools and a JavaScript runtime for machine learning. A Pipcook Pipeline composes scripts and outputs an NPM package containing the trained model and JavaScript functions. The documentation describes a workflow of install, train, test and deploy using the Pipcook command-line tool.
dataschool.io
Data School is a blog about data science and machine learning. Its posts cover topics such as the difference between AI and machine learning, learning scikit-learn, AI progress, and using pandas for calculations. Posts are tagged by subject including Python, AI, and machine learning.
github.com
H2O is an open-source, distributed, fast, and scalable machine learning platform hosted on GitHub by the h2oai organization. It supports a wide range of machine learning methods including deep learning, gradient boosting, random forest, generalized linear modeling, clustering, dimensionality reduction, rule fitting, support vector machines, stacked ensembles, and automated machine learning.
ufldl.stanford.edu
Tutorial teaching the main ideas of unsupervised feature learning and deep learning, with implementation of feature learning and deep learning algorithms. It assumes basic machine learning knowledge, including supervised learning, logistic regression and gradient descent, and suggests completing an earlier machine learning course first.
teachablemachine.withgoogle.com
Teachable Machine provides machine-learning models, research, training resources, or evaluation tools.
Archived, read-only GitHub repository of free resources for learning machine learning with Python and Jupyter Notebook, including notebooks, courses and other links. It was first posted in 2016 and was archived by the owner on Jun 27, 2022.
github.com
PaddlePaddle is an open-source parallel distributed deep learning framework developed from industrial practice, designed for high-performance single-machine and distributed training, as well as cross-platform deployment of deep learning and machine learning models.