<prompt> Hlalosa liphapang tse kholo lipakeng tsa ho ithuta ka mochini, ho ithuta ka botebo le merafo ea data.nan  <response>: Instruction in English: Identify the main differences between machine learning, deep learning and data mining. . Response in English: Machine learning, deep learning, and data mining are interrelated fields that involve the use of computers to recognize patterns and make predictions. However, there are some key differences between them:- Machine learning refers to a subset of artificial intelligence where algorithms are trained on data to make predictions or perform actions. The key idea behind machine learning is that a computer can learn from experience, rather than being explicitly programmed. Machine learning algorithms can be supervised, unsupervised, or semi-supervised.- Deep learning, on the other hand, is a more specific subset of machine learning that involves neural networks with multiple layers. These layers can learn to represent abstract features of the data, allowing for more complex recognition and prediction tasks. Deep learning has been particularly successful for tasks like image recognition, speech recognition, and language translation.- Data mining refers to the process of discovering patterns in large datasets using a variety of methods, including machine learning algorithms. The main goal of data mining is to extract knowledge from data, and it encompasses a wide range of tasks such as clustering, association rule mining, and anomaly detection. Data mining is often used in business and scientific contexts to find hidden relationships and patterns in data.In summary, while machine learning and deep learning focus on training algorithms to make predictions or perform actions, data mining focuses on discovering previously unknown relationships or patterns in data. Deep learning is a more specific type of machine learning that involves training deep neural networks to recognize abstract concepts. . Response in Sesotho: Ho ithuta ka mochini, ho ithuta ka botebo, le ho rafa ha data ke likarolo tse amanang le tsebeliso ea likhomphutha ho lemoha mekhoa le ho bolela esale pele. Leha ho le joalo, ho na le liphapang tse ling tsa bohlokoa lipakeng tsa tsona: - Ho ithuta ka mochini ho bua ka sehlotšoana sa bohlale ba maiketsetso moo li-algorithms li koetlisetsoang ho data ho etsa likhakanyo kapa ho etsa liketso. Taba ea mantlha ea ho ithuta ka mochini ke hore komporo e ka ithuta ho tsoa ho boiphihlelo, ho fapana le ho hlophisoa ka mokhoa o hlakileng. Mekhoa ea ho ithuta ka mochini e ka beoa leihlo, ea se be leihlo, kapa ea behoa leihlo. - Ho ithuta ka botebo, ka lehlakoreng le leng, ke karoloana e ikhethileng ea ho ithuta ka mochini e kenyelletsang marang-rang a neural a nang le likarolo tse ngata. Likarolo tsena li ka ithuta ho emela likarolo tse sa bonahaleng tsa datha, tse lumellang mesebetsi e rarahaneng ea ho lemoha le ho bolela esale pele. Ho ithuta ka botebo ho atlehile haholo mesebetsing e kang temoho ea litšoantšo, temoho ea puo, le phetolelo ea puo. - Morafo oa data o bolela mokhoa oa ho sibolla lipaterone litsebetsong tse kholo tsa data ho sebelisoa mekhoa e fapaneng, ho kenyeletsoa li-algorithms tsa ho ithuta ka mochini. Sepheo se seholo sa ho rafa ha data ke ho ntša tsebo ho data, me e kenyelletsa mefuta e mengata ea mesebetsi e kang ho kopanya, ho tsamaisa melao ea mekhatlo, le ho lemoha ka mokhoa o sa tloaelehang. Hangata meepo ea data e sebelisoa maemong a khoebo le mahlale ho fumana likamano tse patiloeng le lipaterone ho data. Ka kakaretso, ha ho ithuta ka mochini le ho ithuta ka botebo ho tsepamisitse maikutlo ho li-algorithms tsa koetliso ho bolela esale pele kapa ho etsa liketso, ho rafa ha data ho shebana le ho sibolla likamano tse neng li sa tsejoe pele kapa mekhoa ea data. Ho ithuta ka botebo ke mofuta o ikhethileng haholoanyane oa ho ithuta ka mochini o kenyelletsang ho koetlisa marang-rang a tebileng a methapo ea kutlo ho lemoha menahano e sa utloahaleng. 