<prompt> Fana ka mehlala e meli ea batho ba hlokometsoeng le mehlala e meli ea ho ithuta mochine o sa behoang leihlo.nan  <response>: Instruction in English: Provide two examples of supervised and two examples of unsupervised machine learning. . Response in English: Supervised machine learning:1. Classification: An example of supervised learning where an algorithm learns from a dataset containing labeled data, to classify new observations into various classes. One common example is spam email classification where the algorithm is trained to distinguish between spam and non-spam emails.2. Regression: Another example of supervised learning where the algorithm learns to predict a continuous numerical value based on a set of features. An example of regression analysis is predicting house prices based on features such as the number of rooms, size, location, etc.Unsupervised machine learning:1. Clustering: This is an example of unsupervised learning, where the algorithm is used to group similar data points together into clusters based on their features. A common example is customer segmentation in market analysis where customers are grouped into various segments based on their behavior and demographics.2. Dimensionality Reduction: Another example of unsupervised learning where the algorithm is used to reduce the number of features or dimensions in a dataset while preserving its important characteristics. An example is Principal Component Analysis PCA where the algorithm transforms a large set of variables into a smaller set of variables, called principal components, which explain most of the variance in the dataset. . Response in Sesotho: Ho ithuta ka mochini o okametsoeng: 1. Sehlopha: Mohlala oa thuto e hlokometsoeng moo algorithm e ithutang ho tsoa pokellong ea data e nang le data e ngolisitsoeng, ho hlophisa litebello tse ncha ka lihlopha tse fapaneng. Mohlala o mong o tloaelehileng ke ho arola mangolo-tsoibila a spam moo algorithm e koetliselitsoeng ho khetholla pakeng tsa li-imeile tsa spam le tseo e seng tsa spam. 2. Phokotso: Mohlala o mong oa thuto e hlokometsoeng moo algorithm e ithutang ho bolela esale pele boleng bo tsoelang pele ba lipalo bo thehiloeng ho sehlopha sa likarolo. Mohlala oa tlhahlobo ea ho khutlela morao ke ho bolela esale pele litheko tsa ntlo ho latela likarolo tse kang palo ea likamore, boholo, sebaka, joalo-joalo. Thuto ea mochine e sa laoleheng: 1. Ho kopanya: Ona ke mohlala oa thuto e sa laoleheng, moo algorithm e sebelisoang ho hlophisa lintlha tse tšoanang. li supa hammoho ka lihlopha tse ipapisitseng le likarolo tsa tsona. Mohlala o tloaelehileng ke karohano ea bareki tlhahlobisong ea maraka moo bareki ba arotsoeng ka likarolo tse fapaneng ho ipapisitsoe le boitšoaro ba bona le palo ea batho. 2. Phokotso ea Bophahamo: Mohlala o mong oa thuto e sa laoleheng moo algorithm e sebelisetsoang ho fokotsa palo ea likarolo kapa litekanyo ho dataset ha ho ntse ho bolokoa litšobotsi tsa eona tsa bohlokoa. Mohlala ke Principal Component Analysis PCA moo algorithm e fetolang sehlopha se seholo sa mefuta-futa ho sete e nyenyane ea mefuta-futa, e bitsoang likarolo tse ka sehloohong, tse hlalosang boholo ba phapang ea dataset. 