<prompt> hloka ho hlalosa hore na ho ithuta ka mochini ho sebetsa joang.nan  <response>: Instruction in English: You need to explain how machine learning works. . Response in English: Machine learning is a subset of artificial intelligence that involves training a computer system using large amounts of data and algorithms, allowing it to learn and make predictions or take actions based on that data. Its a technique that enables computers to act and make decisions without being explicitly programmed to do so. The goal of machine learning is to develop models that can perform specific tasks by identifying patterns and making predictions from data.Machine learning algorithms can be divided into two main categories: supervised and unsupervised. In supervised learning, the algorithm is trained on a labeled dataset, where the desired outputs are already known. The algorithm is able to learn from this data and improve its performance by comparing its predictions with the known outputs, updating the model accordingly. This type of learning is useful when the goal is to make predictions based on past data. A common example of supervised learning is email spam filtering, where the algorithm is trained to identify spam messages based on labeled data.On the other hand, unsupervised learning involves algorithms that are trained on an unlabeled dataset. They try to learn the underlying structure or patterns within the data without being told what they are looking for. This type of learning is commonly used for clustering and data mining, where the goal is to identify relationships between different data points. An example of unsupervised learning is recommendation systems, where the algorithm tries to identify patterns in users behavior to suggest products or content that may be relevant to them.Overall, machine learning involves feeding large amounts of data to a computer system, using algorithms to learn from that data, and then using the learned model to make predictions or decisions. As more and more data is fed into the system, the accuracy of the model improves, as it identifies patterns and relationships that are not immediately obvious to humans. Its a powerful technique that can be applied to a wide range of applications and is constantly evolving as new techniques and algorithms are developed. . Response in Sesotho: Ho ithuta ka mochini ke karoloana ea bohlale ba maiketsetso bo kenyelletsang ho koetlisa sistimi ea khomphutha e sebelisang lintlha tse ngata le li-algorithms, ho e lumella ho ithuta le ho bolela esale pele kapa ho nka mehato e ipapisitseng le data eo. Ke mokhoa o lumellang likhomphutha ho etsa le ho etsa liqeto ntle le ho hlophisoa ka mokhoa o hlakileng ho etsa joalo. Sepheo sa ho ithuta ka mochine ke ho hlahisa mefuta e ka etsang mesebetsi e itseng ka ho khetholla mekhoa le ho bolela esale pele ho tsoa ho data. Li-algorithms tsa ho ithuta ka mochini li ka aroloa ka mekhahlelo e meli e meholo: e laoloang le e sa laoleheng. Thutong e hlokometsoeng, algorithm e koetlisoa ho dataset e ngotsoeng, moo liphetho tse lakatsehang li seng li tsejoa. Algorithm e khona ho ithuta ho data ena le ho ntlafatsa tsebetso ea eona ka ho bapisa likhakanyo tsa eona le liphetho tse tsebahalang, ho ntlafatsa mohlala ka nepo. Thuto ea mofuta ona e na le thuso ha sepheo e le ho bolela esale pele ho latela lintlha tse fetileng. Mohlala o tloaelehileng oa thuto e hlokometsoeng ke ho sefa spam ka lengolo-tsoibila, moo algorithm e koetliselitsoeng ho tsebahatsa melaetsa ea spam e thehiloeng ho data e ngolisitsoeng. Ka lehlakoreng le leng, ho ithuta ho sa laoleheng ho kenyelletsa litharollo tse koetlisitsoeng ho dataset e sa ngolisoang. Ba leka ho ithuta sebopeho kapa lipaterone tsa motheo ka hara data ntle le ho bolelloa seo ba se batlang. Mofuta ona oa ho ithuta o atisa ho sebelisoa bakeng sa ho kopanya le ho etsa merafo ea data, moo sepheo e leng ho khetholla likamano pakeng tsa lintlha tse fapaneng tsa lintlha. Mohlala oa ho ithuta ho sa hlokomeloe ke litsamaiso tsa likhothaletso, moo algorithm e lekang ho tseba mekhoa ea boitšoaro ba basebelisi ho sisinya lihlahisoa kapa litaba tse ka bang bohlokoa ho bona. Ka kakaretso, ho ithuta ka mochini ho kenyelletsa ho fepa data e ngata ho sistimi ea khomphutha, ho sebelisa li-algorithms ho ithuta ho data eo, ebe o sebelisa mohlala o ithutoang ho etsa likhakanyo kapa liqeto. Ha lintlha tse ngata li ntse li kenngoa tsamaisong, ho nepahala ha mohlala hoa ntlafala, kaha ho khetholla mekhoa le likamano tse sa bonahaleng hang-hang ho batho. Ke mokhoa o matla o ka sebelisoang litsebetsong tse fapaneng mme o lula o fetoha ha mekhoa e mecha le li-algorithms li ntse li ntlafatsoa. 