<prompt> Bapisa le ho bapisa mekhoa e meli e fapaneng ea ho ithuta ka mochini.nan  <response>: Instruction in English: Compare and contrast two different approaches to machine learning. . Response in English: Machine learning is the process of teaching a computer or application to automatically improve its accuracy andor performance by learning from its own experience. There are several approaches to machine learning, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. In this comparison, we will look at supervised learning and unsupervised learning, which are two of the most commonly used approaches.Supervised Learning: In supervised learning, an algorithm is trained using labeled training data, which means that the data provided to the system comes with a desired output label. The algorithm learns by comparing its predictions with the actual output to find errors in order to refine its model. The objective of supervised learning is to predict the outcome for unseen data by finding a relationship between the input and output data. Some of the most common techniques used in supervised learning include decision trees, regression, support vector machines and k-nearest neighbors. Supervised learning is often used in scenarios such as classification and regression problems.Unsupervised Learning: In contrast, unsupervised learning involves training the algorithm with data that is not labeled or classified, meaning the outcome or result is unknown. The algorithm is left to find patterns and relationships within the data on its own. The goal of unsupervised learning is to identify the underlying structure within the data and group similar data points together. Some of the most common techniques used in unsupervised learning are clustering, association and dimensionality reduction. Unsupervised learning is often used in scenarios such as market segmentation, anomaly detection or to identify hidden patterns in data.In summary, supervised learning and unsupervised learning both involve using data to teach an algorithm to make predictions, however the difference lies in the type of data provided to the system. Supervised learning uses labeled data, and the main goal is prediction, while unsupervised learning uses unlabeled data, and the main goal is discovering the underlying structure of the data. Both approaches have their own set of advantages and are chosen based on the specific problem to be addressed. . Response in Sesotho: Ho ithuta ka mochini ke mokhoa oa ho ruta komporo kapa sesebelisoa ho ntlafatsa ka bohona ho nepahala lekapa tšebetso ka ho ithuta ho tsoa ho boiphihlelo ba eona. Ho na le mekhoa e maloa ea ho ithuta ka mochini, ho kenyelletsa ho ithuta ho hlokometsoeng, ho ithuta ho sa hlokomeloe, ho matlafatsa thuto le ho ithuta ka botebo. Papisong ena, re tla sheba thuto e hlokometsoeng le thuto e sa laoleheng, e leng mekhoa e meli e sebelisoang haholo. Thuto e Laolehileng: Thutong e hlokometsoeng, algorithm e koetlisoa ho sebelisoa lintlha tsa koetliso tse ngotsoeng, e leng se bolelang hore lintlha tse fanoeng tsamaisong li tla le label ea tlhahiso e lakatsehang. Algorithm e ithuta ka ho bapisa likhakanyo tsa eona le sephetho sa nete ho fumana liphoso molemong oa ho ntlafatsa mohlala oa eona. Sepheo sa thuto e hlokometsoeng ke ho bolela esale pele sephetho sa data e sa bonahaleng ka ho fumana kamano pakeng tsa lintlha tse kenang le tse hlahisoang. Mekhoa e meng e atileng haholo e sebelisoang thutong e hlokometsoeng e kenyelletsa lifate tsa liqeto, ho theola maemo, mechini ea li-vector ea tšehetso le baahelani ba haufi le k. Thuto e hlokometsoeng e atisa ho sebelisoa maemong a kang mathata a lihlopha le a ho khutlela morao. Ho Ithuta ho sa Lebelloa: Ka lehlakoreng le leng, thuto e sa laoleheng e kenyelletsa ho koetlisa algorithm ka data e sa ngolisoang kapa e sa hlophisoang, ho bolelang hore sephetho kapa sephetho ha se tsejoe. Algorithm e setse ho fumana lipaterone le likamano ka hara data ka boyona. Sepheo sa thuto e sa laoleheng ke ho tsebahatsa sebopeho sa motheo ka hara data le ho bokella lintlha tse tšoanang tsa data hammoho. Tse ling tsa mekhoa e sebelisoang haholo thutong e sa laoleheng ke ho kopanya, ho kopanya le ho fokotsa boholo. Ho ithuta ho sa hlokomeloe hangata ho sebelisoa maemong a kang karohano ea maraka, ho lemoha ka mokhoa o sa tloaelehang kapa ho tseba mekhoa e patiloeng ho data. Ka kakaretso, thuto e laoloang le thuto e sa laoleheng ka bobeli li kenyelletsa ho sebelisa data ho ruta algorithm ho etsa likhakanyo, leha ho le joalo phapang e teng mofuteng oa data o fanoeng tsamaisong. Thuto e hlokometsoeng e sebelisa lintlha tse ngotsoeng, me sepheo se seholo ke ho bolela esale pele, ha thuto e sa laoleheng e sebelisa boitsebiso bo sa ngolisoang, me sepheo se seholo ke ho sibolla sebopeho sa motheo sa data. Mekhoa ena ka bobeli e na le melemo ea eona me e khethoa ho latela bothata bo itseng bo lokelang ho rarolloa. 