<prompt> Hlalosa mokhoa oa motheo oa ho Ithuta ka Mechininan  <response>: Instruction in English: Describe the basic methodology of Machine Learning . Response in English: Machine learning is a form of artificial intelligence that involves developing algorithms that can learn from and make predictions or decisions based on data. The basic methodology of machine learning can be described in a few steps:1. Data collection and preparation: The first step in the machine learning process involves collecting and preparing data that will be used to train and test the machine learning algorithms. The data is typically divided into two sets: training data and testing data. The training data is used to train the algorithms, while the testing data is used to evaluate their performance.2. Feature selection: Once the data has been collected and prepared, the next step involves selecting the relevant features or variables that will be used by the algorithms to make predictions or decisions. This step is important because not all features may be relevant or useful for the machine learning task at hand.3. Model selection and training: The next step involves selecting a suitable machine learning model or algorithm and training it using the training data. Some of the popular machine learning models include decision trees, support vector machines, neural networks, and logistic regression, among others. During the training process, the algorithm learns from the data and adjusts its internal parameters to make accurate predictions.4. Model evaluation: Once the model has been trained, the next step is to evaluate its performance using the testing data. Some of the common performance metrics used to evaluate machine learning models include accuracy, precision, recall, and F1 score, among others.5. Model deployment and prediction: Once the model has been trained and evaluated, it can then be used to make predictions or decisions on new data. The model can be deployed in a variety of settings, such as on a website, a mobile app, or an Internet of Things IoT device.In summary, the basic methodology of machine learning involves collecting and preparing data, selecting relevant features, selecting and training a suitable machine learning model, evaluating its performance, and finally deploying it to make predictions or decisions on new data. . Response in Sesotho: Ho ithuta ka mochini ke mofuta oa bohlale ba maiketsetso o kenyelletsang ho etsa li-algorithms tse ka ithutang le ho etsa likhakanyo kapa liqeto tse thehiloeng ho data. Mokhoa oa motheo oa ho ithuta mochine o ka hlalosoa ka mehato e seng mekae: 1. Ho bokella le ho lokisa lintlha: Mohato oa pele oa mokhoa oa ho ithuta mochine o kenyelletsa ho bokella le ho lokisetsa lintlha tse tla sebelisoa ho koetlisa le ho leka mekhoa ea ho ithuta mochine. Lintlha li arotsoe ka lihlopha tse peli: data ea koetliso le data ea liteko. Lintlha tsa koetliso li sebelisetsoa ho koetlisa li-algorithms, ha lintlha tsa tlhahlobo li sebelisetsoa ho lekola tsebetso ea tsona. 2. Khetho ea likarolo: Ha lintlha li se li bokelitsoe le ho lokisoa, mohato o latelang o kenyelletsa ho khetha likarolo tse amehang kapa mefuta-futa e tla sebelisoa ke li-algorithms ho etsa likhakanyo kapa liqeto. Mohato ona o bohlokoa hobane ha se likarolo tsohle tse ka bang bohlokoa kapa tsa thusa bakeng sa mosebetsi oa ho ithuta mochini o teng. 3. Khetho ea mohlala le koetliso: Mohato o latelang o kenyelletsa ho khetha mohlala o loketseng oa ho ithuta mochine kapa algorithm le ho o koetlisa ho sebelisa lintlha tsa koetliso. E meng ea mefuta e tsebahalang ea ho ithuta ka mochini e kenyelletsa lifate tsa liqeto, mechini ea li-vector tse tšehetsang, marang-rang a neural, le ho fokotseha ha lintho, hara tse ling. Nakong ea tsebetso ea koetliso, algorithm e ithuta ho tsoa ho data mme e fetola maemo a eona a kahare ho etsa likhakanyo tse nepahetseng. 4. Tlhahlobo ea mohlala: Hang ha mohlala o se o koetlisoa, mohato o latelang ke ho hlahloba tsebetso ea oona ho sebelisa lintlha tsa tlhahlobo. Tse ling tsa metrics e tloaelehileng ea tsebetso e sebelisoang ho lekola mefuta ea ho ithuta ea mochini e kenyelletsa ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1, hara tse ling. 5. Ho tsamaisoa le ho bolela esale pele mohlala: Hang ha mohlala o se o koetlisitsoe le ho hlahlojoa, o ka sebelisoa ho etsa likhakanyo kapa liqeto mabapi le lintlha tse ncha. Moetso ona o ka sebelisoa maemong a fapaneng, joalo ka webosaete, sesebelisoa sa mohala, kapa sesebelisoa sa Marang-rang sa Lintho IoT. Ka kakaretso, mokhoa oa motheo oa ho ithuta ka mochine o kenyelletsa ho bokella le ho lokisa lintlha, ho khetha likarolo tse amehang, ho khetha le ho koetlisa mohlala o loketseng oa ho ithuta mochine, ho lekola tsebetso ea oona, me qetellong o sebelisetsoe ho etsa likhakanyo kapa liqeto ka lintlha tse ncha. 