<prompt> Hlalosa phapang pakeng tsa thuto e hlokometsoeng le e sa behoang leihlo ka lipolelo tse 5-8.nan  <response>: Instruction in English: Explain the differences between supervised and unsupervised learning in 5-8 sentences. . Response in English: Supervised learning and unsupervised learning are two of the main approaches to machine learning. Supervised learning involves using a labeled dataset to train a model to make accurate predictions or decisions. The model is given inputoutput pairs as training data and learns to generalize from these examples to make predictions about new, unseen data. In contrast, unsupervised learning does not use labeled data. Instead, the algorithm must find structure in the data on its own, without any explicit guidance. This can involve clustering data into groups based on similarity, or finding patterns and relationships between variables. As such, supervised learning is often used for predictive modeling, while unsupervised learning is commonly used for exploratory data analysis and uncovering hidden structures within data. . Response in Sesotho: Thuto e hlokometsoeng le thuto e sa laoleheng ke mekhoa e meli ea mantlha ea ho ithuta ka mochini. Thuto e hlokometsoeng e kenyelletsa ho sebelisa dataset e ngolisitsoeng ho koetlisa mohlala ho etsa likhakanyo kapa liqeto tse nepahetseng. Moetso o fuoa li-inputoutput pairs e le data ea lithupelo me o ithuta ho akaretsa ho tsoa mehlala ena ho etsa likhakanyo ka lintlha tse ncha, tse sa bonahaleng. Ka lehlakoreng le leng, thuto e sa laoleheng ha e sebelise lintlha tse ngotsoeng. Sebakeng seo, algorithm e tlameha ho fumana sebopeho ho data ka boyona, ntle le tataiso e hlakileng. Sena se ka kenyelletsa ho bokella lintlha ka lihlopha tse ipapisitseng le ho tšoana, kapa ho fumana mekhoa le likamano lipakeng tsa mefuta e fapaneng. Ka hona, thuto e hlokometsoeng e atisa ho sebelisoa bakeng sa mohlala o lebisitsoeng pele, ha thuto e sa laoleheng e atisa ho sebelisoa bakeng sa tlhahlobo ea lintlha tsa lipatlisiso le ho senola mekhoa e patehileng ka hara data. 