<prompt> Ho fanoe ka mofuta ofe kapa ofe oa data, hlalosa mohopolo oa ho tloaeleha.nan  <response>: Instruction in English: Given any type of data, explain the concept of normalization. . Response in English: Normalization is a process that is commonly used in data preprocessing to bring values measured on different scales to a common scale. It is often necessary when features in the dataset have different ranges or units, making it difficult for some machine learning algorithms to learn effectively. Normalization works by rescaling data, so that it falls within a specific range, usually between 0 and 1.There are several methods to normalize data, including min-max normalization, mean normalization, and z-score normalization. Each of these methods works by applying a different mathematical formula to the data to standardize its scale.Min-max normalization, for example, rescales all values so they fall between 0 and 1. This is achieved by subtracting the minimum value in the feature from each data point, then dividing the result by the range of the feature. Mean normalization, on the other hand, involves subtracting the mean value of the feature from each data point, then dividing the result by the range of the feature. This results in the data being centered around zero.Z-score normalization, also known as standardization, involves subtracting the mean value of the feature from each data point, then dividing the result by the standard deviation of the feature. This results in the data having a standard normal distribution, with a mean of zero and a standard deviation of one.In conclusion, normalization is an important technique in data preprocessing that helps to standardize the scale of data, making it more suitable for certain machine learning algorithms to work with. . Response in Sesotho: Ho tloaeleha ke mokhoa o atisang ho sebelisoa ha ho etsoa data preprocessing ho tlisa litekanyetso tse lekantsoeng ka litekanyo tse fapaneng ho ea boemong bo tloaelehileng. Hangata hoa hlokahala ha likarolo tsa sephutheloana li na le mefuta e fapaneng kapa li-unit, e leng ho etsang hore ho be thata ho li-algorithms tse ling tsa ho ithuta ka mochini ho ithuta ka katleho. Normalization e sebetsa ka ho tsosolosa data, e le hore e oele ka hara mefuta e itseng, hangata e pakeng tsa 0 le 1. Ho na le mekhoa e mengata ea ho etsa hore data e tloaelehe, ho akarelletsa le min-max normalization, ho bolela ho tloaeleha, le ho tloaeleha ha z-score. E ngoe le e ngoe ea mekhoa ena e sebetsa ka ho sebelisa mokhoa o fapaneng oa lipalo ho data ho tiisa sekala sa eona. Ka mohlala, Min-max normalization, e khutlisa litekanyetso tsohle e le hore li oele pakeng tsa 0 le 1. Sena se finyelloa ka ho tlosa boleng bo fokolang ba sebopeho ho tloha sebakeng se seng le se seng sa data, ebe o arola sephetho ka mefuta e mengata ea sebopeho. Ho bolela ho tloaeleha, ka lehlakoreng le leng, ho kenyelletsa ho tlosa boleng ba boleng ba karolo ho tloha sebakeng se seng le se seng sa data, ebe o arola sephetho ka mefuta e mengata ea sebopeho. Sena se fella ka hore data e behoe ho zero. Z-score normalization, eo hape e tsejoang e le standardization, e kenyelletsa ho tlosa boleng ba boleng ba karolo sebakeng se seng le se seng sa data, ebe o arola sephetho ka ho kheloha ho tloaelehileng ha karolo. Sena se fella ka hore data e be le kabo e tloaelehileng e tloaelehileng, e nang le moelelo oa zero le ho kheloha ho tloaelehileng ho e le ngoe. Qetellong, ho tloaeleha ke mokhoa oa bohlokoa tsebetsong ea data preprocessing e thusang ho tiisa tekanyo ea data, e leng se etsang hore e be e loketseng haholoanyane bakeng sa li-algorithms tse itseng tsa ho ithuta mochine ho sebetsa le tsona. 