<prompt> Fana ka mehlala e meli bakeng sa mathata a thuto a sa behoang leihlo.nan  <response>: Instruction in English: Provide two examples for unsupervised learning problems. . Response in English: 1. Clustering - One common application of unsupervised learning is clustering, where the algorithm is used to group similar data points together. For example, a retail store may use clustering to categorize their customers into different segments based on their purchasing behavior. The algorithm will analyze the data and identify patterns and relationships between the variables to create the groups.2. Dimensionality Reduction - Another unsupervised learning problem is dimensionality reduction, where the algorithm is used to reduce the number of variables in a dataset while retaining the essential information. For example, a medical research team may collect a large amount of data from various tests and measurements on patients. They can use dimensionality reduction techniques to simplify the data and identify the most important variables or factors that affect the outcome of a particular disease. This can help to reduce the complexity of the data analysis process and improve the accuracy of the results. . Response in Sesotho: 1. Ho kopanya - Tšebeliso e ngoe e tloaelehileng ea thuto e sa laoleheng ke ho kopanya, moo algorithm e sebelisoang ho kopanya lintlha tse tšoanang tsa data hammoho. Mohlala, lebenkele la mabenkele le ka sebelisa clustering ho arola bareki ba bona ka likarolo tse fapaneng ho latela mokhoa oa bona oa ho reka. Algorithm e tla hlahloba lintlha le ho khetholla mekhoa le likamano pakeng tsa mefuta-futa ho theha lihlopha. 2. Phokotso ea Bophahamo - Bothata bo bong bo sa laoleheng ba ho ithuta ke ho fokotseha ha dimensionality, moo algorithm e sebelisetsoang ho fokotsa palo ea mefuta-futa ea dataset ha ho ntse ho bolokoa boitsebiso ba bohlokoa. Mohlala, sehlopha sa lipatlisiso tsa bongaka se ka bokella lintlha tse ngata ho tsoa litekong le litekanyo tse fapaneng ho bakuli. Ba ka sebelisa mekhoa ea ho fokotsa li-dimensionality ho nolofatsa lintlha le ho khetholla mefuta-futa ea bohlokoa ka ho fetisisa kapa lintlha tse amang phello ea lefu le itseng. Sena se ka thusa ho fokotsa ho rarahana ha tsebetso ea tlhahlobo ea data le ho ntlafatsa ho nepahala ha liphetho. 