Weak supervision is a method of training machine learning models using data that is labelled with less accuracy or detail than traditional hand-labelled datasets. Instead of relying solely on expensive, manually created labels, weak supervision uses noisier, incomplete, or indirect sources of information. These sources can include rules, heuristics, crowd-sourced labels, or existing but imperfect…

An Active Learning Framework is a structured approach used in machine learning where the algorithm selects the most useful data points to learn from, rather than using all available data. This helps the model become more accurate with fewer labelled examples, saving time and resources. It is especially useful when labelling data is expensive or…

Crowdsourced data labelling is a process where many individuals, often recruited online, help categorise or annotate large sets of data such as images, text, or audio. This approach makes it possible to process vast amounts of information quickly and at a lower cost compared to hiring a small group of experts. It is commonly used…

A data augmentation framework is a set of tools or software that helps create new versions of existing data by making small changes, such as rotating images or altering text. These frameworks are used to artificially expand datasets, which can help improve the performance of machine learning models. By providing various transformation techniques, a data…