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OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
In 2011, MIT OpenCourseWare introduced the first of fifteen OCW Scholar courses, which are designed specifically for the needs of independent learners. While still publications of course materials like the rest of the site content, these courses are more in-depth and the materials are presented in logical sequences that facilitate self-study.
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]
Ng taught one of these courses, "Machine Learning", which includes his video lectures, along with the student materials used in the Stanford CS229 class. It offered a similar experience to MIT OpenCourseWare , except it aimed at providing a more "complete course" experience, equipped with lectures, course materials, problems and solutions, etc.
However, selecting and tuning an algorithm for training on unseen data requires significant experimentation. Robustness: If the model, cost function and learning algorithm are selected appropriately, the resulting ANN can become robust. Neural architecture search (NAS) uses machine learning to automate ANN design. Various approaches to NAS have ...
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values ...
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