<prompt> Hlalosa hore na mechini e ithuta joangnan  <response>: Instruction in English: Explain how machines learn . Response in English: Machine learning is a subset of artificial intelligence that involves training algorithms to find patterns and produce predictions based on data. The basic idea is to use data to teach machines to make decisions autonomously, without explicit programming.The process of machine learning starts with the collection of data. This data is then pre-processed to remove noise and outliers, and is split into two sets- a training set and a testing set. The training set is used to train the machine learning algorithm, while the testing set is used to evaluate its performance.There are three main types of machine learning: supervised, unsupervised, and reinforcement learning. In supervised learning, the algorithm is trained using labeled data, where the desired output is already known. Then, given new data, the algorithm uses the patterns it has learned from the training set to make predictions.In unsupervised learning, the algorithm is given raw, unlabeled data, and it must find patterns and relationships on its own. It is often used for data exploration or clustering.Reinforcement learning involves an agent interacting with an environment, learning through trial and error. The agent is given rewards or punishments based on its actions, and its goal is to maximize its total reward.Machine learning algorithms can learn from new data through a process called backpropagation. The algorithms error, or the difference between its output and the desired output, is calculated and the weights of the connections between the nodes of the algorithm are adjusted to minimize this error. This process continues until the algorithm has learned the relationship between the input and output data.Overall, machine learning is a powerful tool that can be used for tasks such as prediction, classification, and data analysis. As more data is collected and our algorithms improve, the potential applications of machine learning continue to grow. . Response in Sesotho: Ho ithuta ka mochini ke karoloana ea bohlale ba maiketsetso bo kenyelletsang li-algorithms tsa koetliso ho fumana lipaterone le ho hlahisa likhakanyo tse thehiloeng ho data. Mohopolo oa mantlha ke ho sebelisa data ho ruta mechini ho etsa liqeto ka boikemelo, ntle le mananeo a hlakileng. Mokhoa oa ho ithuta ka mochini o qala ka pokello ea lintlha. Joale data ena e sebetsoa esale pele ho tlosa lerata le li-outliers, me e arotsoe ka lihlopha tse peli- sete ea koetliso le sete ea liteko. Sehlopha sa koetliso se sebelisetsoa ho koetlisa algorithm ea ho ithuta mochine, ha sete ea liteko e sebelisetsoa ho lekola tsebetso ea eona. Ho na le mefuta e meraro ea mantlha ea ho ithuta ka mochini: ho ithuta ho hlokomeloa, ho sa laoloe, le ho matlafatsa thuto. Thutong e hlokometsoeng, algorithm e koetlisetsoa ho sebelisa lintlha tse ngotsoeng, moo tlhahiso e lakatsehang e seng e tsejoa. Joale, ho fanoe ka lintlha tse ncha, algorithm e sebelisa mekhoa eo e ithutileng eona ho tloha thupelong e behiloeng ho etsa likhakanyo. Thutong e sa laoleheng, algorithm e fuoa lintlha tse tala, tse sa ngolisoang, me e tlameha ho fumana mekhoa le likamano ka boeona. Hangata e sebelisoa bakeng sa ho hlahloba lintlha kapa ho kopanya. Ho ithuta ka matlafatso ho kenyelletsa moemeli ea sebelisanang le tikoloho, ho ithuta ka liteko le liphoso. Moemeli o fuoa meputso kapa likotlo tse ipapisitseng le liketso tsa hae, me sepheo sa hae ke ho eketsa moputso oa hae kaofela. Li-algorithms tsa ho ithuta ka mochini li ka ithuta ho data e ncha ka mokhoa o bitsoang backpropagation. Phoso ea algorithm, kapa phapang pakeng tsa tlhahiso ea eona le tlhahiso e lakatsehang, e baloa me boima ba likhokahano pakeng tsa nodes ea algorithm bo lokisoa ho fokotsa phoso ena. Tsebetso ena e tsoela pele ho fihlela algorithm e ithutile kamano pakeng tsa data ea ho kenya le ho tsoa. Ka kakaretso, ho ithuta ka mochini ke sesebelisoa se matla se ka sebelisetsoang mesebetsi e kang ho bolela esale pele, ho hlopha, le tlhahlobo ea data. Ha lintlha tse ling li ntse li bokelloa me li-algorithms tsa rona li ntlafala, tsebeliso e ka bang teng ea ho ithuta mochini e ntse e hola. 