<prompt> Bapisa tsebeliso ea ho Ithuta ka Mechini le Thuto e Tebileng.nan  <response>: Instruction in English: Compare the uses of Machine Learning and Deep Learning. . Response in English: Machine Learning ML and Deep Learning DL are two subsets of Artificial Intelligence that have garnered a lot of attention in recent years. At their core, both involve training machines to learn from data, in order to make intelligent decisions and predictions. However, there are some fundamental differences in the way ML and DL are used, their architectures, and their applications.Machine Learning uses algorithms to analyze and learn from data, in order to make informed decisions. These algorithms can be broken down into two main types - supervised and unsupervised learning. Supervised learning involves training a model on a labeled data-set, where both inputs and outputs are known, and the algorithm learns the relationship between them. Unsupervised learning, on the other hand, deals with unlabeled data, where the algorithm must find patterns and relationships on its own. ML has a wide range of applications, from recommendation systems and image recognition, to natural language processing and even market analysis.Deep Learning, a subset of Machine Learning, is inspired by the structure and function of the human brain. It uses artificial neural networks ANNs to learn and make decisions. These neural networks consist of layers of interconnected nodes, where each layer processes the information received from the previous layer, and passes it on to the next. This multi-layered architecture allows DL to achieve incredible accuracy in tasks such as image and speech recognition, and natural language processing.In summary, while both Machine Learning and Deep Learning are used for similar end goals, their architectures and methods vary. Deep Learning is usually more complex and requires more data to train, but can achieve higher accuracy in certain tasks. On the other hand, Machine Learning might be more suitable for less complex tasks, or for cases when the data-set is smaller. However, both ML and DL have proven to be incredibly powerful tools, and their uses will continue to expand and evolve as the field of AI progresses. . Response in Sesotho: Ho Ithuta ka Mochini ML le Ho Ithuta ho Tebileng DL ke likaroloana tse peli tsa Artificial Intelligence tse hapileng tlhokomelo e ngata lilemong tsa morao tjena. Motheong oa bona, ka bobeli li kenyelletsa mechini ea koetliso ho ithuta ho data, e le ho etsa liqeto tse bohlale le ho bolela esale pele. Leha ho le joalo, ho na le liphapang tsa mantlha tseleng eo ML le DL li sebelisoang ka eona, meaho ea tsona le tsebeliso ea tsona. Ho Ithuta ka Mechini ho sebelisa li-algorithms ho sekaseka le ho ithuta ho tsoa ho data, molemong oa ho etsa liqeto tse nang le tsebo. Li-algorithms tsena li ka aroloa ka mefuta e meli e meholo - thuto e hlokometsoeng le e sa laoleheng. Boithuto bo hlokometsoeng bo kenyelletsa ho koetlisa mohlala ho sete ea data e ngolisitsoeng, moo lintlha le liphetho li tsejoang, me algorithm e ithuta kamano lipakeng tsa tsona. Ho ithuta ho sa laoleheng, ka lehlakoreng le leng, ho sebetsana le lintlha tse sa ngolisoang, moo algorithm e tlamehang ho fumana mekhoa le likamano ka boeona. ML e na le mefuta e mengata ea litsebetso, ho tloha litsebetsong tsa likhothaletso le temoho ea litšoantšo, ho isa ho tsebetso ea puo ea tlhaho esita le tlhahlobo ea maraka. Ho Ithuta ho Tebileng, karoloana ea ho Ithuta ka Mechini, e susumetsoa ke sebopeho le tšebetso ea boko ba motho. E sebelisa marang-rang a maiketsetso a neural ANNs ho ithuta le ho etsa liqeto. Marang-rang ana a neural a na le lihlopha tsa li-node tse hokahaneng, moo karolo e ngoe le e ngoe e sebetsanang le boitsebiso bo fumanoeng ho tloha karolong e fetileng, ebe e e fetisetsa ho e latelang. Mehaho ena e nang le mekhahlelo e mengata e lumella DL ho fumana ho nepahala ho makatsang mesebetsing e kang ho lemoha litšoantšo le puo, le ho sebetsana le puo ea tlhaho. Ka kakaretso, le hoja ho Ithuta ka Mochini le Thuto e Tebileng li sebelisoa bakeng sa lipheo tse tšoanang tsa ho qetela, meaho le mekhoa ea tsona lia fapana. Thuto e tebileng hangata e rarahane haholo me e hloka boitsebiso bo bongata ho koetlisa, empa e ka finyella ho nepahala ho phahameng mesebetsing e itseng. Ka lehlakoreng le leng, ho Ithuta ka Mechini ho ka ba molemo bakeng sa mesebetsi e sa rarahanang haholo, kapa bakeng sa linyeoe ha sete ea data e le nyane. Leha ho le joalo, ML le DL ka bobeli li ipakile e le lisebelisoa tse matla haholo, me tsebeliso ea tsona e tla tsoela pele ho hola le ho fetoha ha lefapha la AI le ntse le tsoela pele. 