<prompt> Ho ithuta ka Mochini ho ka sebelisoa joang ho tsebahatsa li-imeile tsa spam?nan  <response>: Instruction in English: How can Machine Learning be used to identify spam emails? . Response in English: Machine Learning can be used to identify spam emails by developing an algorithm that can learn to recognize common patterns and characteristics of unwanted emails. This is usually done by feeding the algorithm with large amounts of data consisting of both spam and non-spam emails. The algorithm can then analyze these data, identify patterns that are unique to spam emails, such as the use of certain words or phrases, sentence structure, or formatting, and use this knowledge to accurately classify future emails as spam or not spam.One common approach to spam email detection using machine learning is the use of supervised learning, where the algorithm is trained using a pre-labeled data set of known spam and non-spam emails. The algorithm then uses this training data to develop a model that can accurately classify new emails as either spam or not spam.One of the most popular techniques used in this approach is the Naive Bayes classifier, which applies probabilistic principles to determine the likelihood that a given email is spam or not. Other techniques that can be used include decision trees, support vector machines, and neural networks.Machine learning algorithms can also be used to continuously improve the accuracy of spam detection by adapting to changes in spamming techniques. As new types of spam emails are developed, the algorithm can analyze these new data and modify its model to accurately identify these new types of spam.In summary, Machine Learnings capability of finding patterns and adapting to new information is vital in identifying spam emails, and it is a widely used technique in email providers efforts to keep their users inboxes clean. . Response in Sesotho: Ho Ithuta ka Mechini ho ka sebelisoa ho tsebahatsa li-imeile tsa spam ka ho theha algorithm e ka ithutang ho lemoha mekhoa e tloaelehileng le litšobotsi tsa li-imeile tse sa batleheng. Hangata sena se etsoa ka ho fepa algorithm ka lintlha tse ngata tse nang le li-imeile tsa spam le tseo e seng tsa spam. Joale algorithm e ka hlahloba lintlha tsena, ea khetholla mekhoa e ikhethang ho li-imeile tsa spam, tse kang tšebeliso ea mantsoe kapa lipoleloana tse itseng, sebopeho sa polelo, kapa mokhoa oa ho hlophisa, me e sebelise tsebo ena ho arola ka nepo li-imeile tsa nakong e tlang e le spam kapa eseng spam. Tsela e ngoe e tloaelehileng ea ho lemoha melaetsa ea spam ka ho sebelisa mochine oa ho ithuta ke tšebeliso ea thuto e hlokometsoeng, moo algorithm e koetliselitsoeng ho sebelisa sete ea data e ngotsoeng esale pele ea li-spam tse tsebahalang le li-imeile tseo e seng tsa spam. Ka mora moo, algorithm e sebelisa lintlha tsena tsa koetliso ho hlahisa mohlala o ka khethollang mangolo-tsoibila a macha ka nepo e le spam kapa eseng spam. E ngoe ea mekhoa e tsebahalang haholo e sebelisoang mokhoeng ona ke Naive Bayes classifier, e sebelisang melao-motheo ea probabilistic ho fumana monyetla oa hore lengolo-tsoibila le fanoeng ke spam kapa che. Mekhoa e meng e ka sebelisoang e kenyelletsa lifate tsa liqeto, mechini ea li-vector ea tšehetso, le marang-rang a neural. Mekhoa ea ho ithuta ka mochine e ka boela ea sebelisoa ho tsoela pele ho ntlafatsa ho nepahala ha ho fumanoa ha spam ka ho ikamahanya le liphetoho tsa mekhoa ea spamming. Ha mefuta e mecha ea li-imeile tsa spam e ntse e ntlafatsoa, algorithm e ka hlahloba lintlha tsena tse ncha le ho fetola mohlala oa eona ho khetholla ka nepo mefuta ena e mecha ea spam. Ka bokhutšoanyane, bokhoni ba ho Ithuta ka Mochini ba ho fumana mekhoa le ho ikamahanya le lintlha tse ncha bo bohlokoa ho tsebahatseng li-imeile tsa spam, me ke mokhoa o sebelisoang haholo boitekong ba bafani ba mangolo-tsoibila ho boloka li-inbox tsa basebelisi ba bona li hloekile. 