<prompt> Bapisa le ho bapisa liphapang lipakeng tsa ho ithuta ka mochini o hlokometsoeng le o sa behoang leihlo.nan  <response>: Instruction in English: Compare and contrast the differences between supervised and unsupervised machine learning. . Response in English: Supervised and unsupervised machine learning are two of the main approaches used in artificial intelligence and data analysis. Both are used to discover patterns and make predictions from data, but there are some key differences between the two.Supervised machine learning involves working with a labeled data set, where the desired output is already known. An algorithm is trained on this data, and through the use of labeled examples, the algorithm can determine the relationship between the input data and the output data. Once trained, the model can make accurate predictions on new data. Supervised learning algorithms can be used for both classification and regression problems. Some examples of supervised learning algorithms include Decision Trees, Linear Regression, and Naive Bayes.Unsupervised machine learning, on the other hand, does not involve labeled data. Instead, the algorithm works to find structure and relationships within the data on its own, often grouping similar data points together based on their characteristics. Unlike supervised learning, unsupervised learning is not used to predict a desired output, but rather to discover hidden patterns or groupings within the data. Some examples of unsupervised learning algorithms include clustering, such as K-Means, and dimensionality reduction techniques, like Principal Component Analysis.The key difference between supervised and unsupervised learning lies in the use of labeled data. Supervised learning requires labeled data in order to train the model and make accurate predictions, while unsupervised learning does not. Additionally, supervised learning is used to predict a specific outcome, while unsupervised learning is used to discover underlying patterns or structures within the data. Both approaches have their own strengths and weaknesses, making them suitable for different types of problems. . Response in Sesotho: Ho ithuta ka mochini o hlokometsoeng le o sa laoleheng ke mekhoa e meli ea mantlha e sebelisoang ho bohlale ba maiketsetso le tlhahlobo ea data. Ka bobeli li sebelisetsoa ho sibolla lipaterone le ho etsa likhakanyo ho tsoa ho data, empa ho na le phapang ea bohlokoa lipakeng tsa tse peli. Ho ithuta ka mochini o hlokometsoeng ho kenyelletsa ho sebetsa ka sete ea data e ngolisitsoeng, moo tlhahiso e batloang e seng e tsejoa. Algorithm e koetliselitsoe ho data ena, me ka tšebeliso ea mehlala e ngotsoeng, algorithm e ka tseba kamano pakeng tsa lintlha tsa ho kenya le lintlha tse hlahisoang. Ha e se e koetlisitsoe, mohlala o ka etsa likhakanyo tse nepahetseng ho data e ncha. Litaolo tsa ho ithuta tse hlokometsoeng li ka sebelisoa bakeng sa mathata a lihlopha le a ho khutlela morao. Mehlala e meng ea li-algorithms tsa ho ithuta tse hlokometsoeng li kenyelletsa Decision Trees, Linear Regression, le Naive Bayes. Ka lehlakoreng le leng, ho ithuta ka mochini ho sa laoleheng ha ho kenyelle data e ngolisitsoeng. Ho e-na le hoo, algorithm e sebetsa ho fumana sebopeho le likamano ka hare ho data ka boeona, hangata e kopanya lintlha tse tšoanang tsa data hammoho ho latela litšobotsi tsa tsona. Ho fapana le thuto e hlokometsoeng, thuto e sa laoleheng ha e sebelisoe ho bolela esale pele sephetho se lakatsehang, empa ho e-na le ho sibolla mekhoa e patiloeng kapa lihlopha ka hara data. Mehlala e meng ea mekhoa ea ho ithuta e sa hlokomeloe e kenyelletsa ho kopanya, joalo ka K-Means, le mekhoa ea ho fokotsa boholo, joalo ka Principal Component Analysis. Phapang e ka sehloohong lipakeng tsa thuto e hlokometsoeng le e sa behoang leihlo ke tšebeliso ea lintlha tse ngotsoeng. Thuto e hlokometsoeng e hloka boitsebiso bo ngotsoeng e le ho koetlisa mohlala le ho etsa likhakanyo tse nepahetseng, athe thuto e sa laoleheng ha e etse joalo. Ho feta moo, thuto e hlokometsoeng e sebelisetsoa ho bolela esale pele sephetho se itseng, ha thuto e sa laoleheng e sebelisetsoa ho sibolla mekhoa kapa libopeho tse teng ka hara data. Mekhoa ena ka bobeli e na le matla le mefokolo ea eona, e etsang hore e tšoanele mefuta e fapaneng ea mathata. 