<prompt> Bolela mekhahlelo ea lipeipi tsa ho ithuta ka mochini.nan  <response>: Instruction in English: Name the stages of the machine learning pipeline. . Response in English: The main stages of the Machine Learning pipeline are:1. Data collection: Gathering raw data from various sources.2. Data preprocessing: Cleaning, organizing, and transforming data to ensure it can be used effectively for machine learning.3. Feature engineering: Selecting and preparing appropriate features that best represent the data for building the model.4. Model selection: Selecting the appropriate machine learning algorithm that is best suited for the specific problem.5. Model training: Training the selected algorithm on the preprocessed and engineered data.6. Model validation: Validating the trained model on a set of data which it hasnt seen before to gauge its performance.7. Hyperparameter tuning: Fine-tuning the algorithms parameters to improve its performance.8. Model evaluation: Evaluating the models performance using appropriate metrics.9. Model deployment: Deploying the model for use, either by integrating it into an existing system or making it available through an API.10. Model monitoring and maintenance: Continuously monitoring and updating the model to ensure it remains relevant and provides accurate predictions. . Response in Sesotho: Mehato e ka sehloohong ea phaephe ea ho Ithuta ka Mochini ke: 1. Pokello ea lintlha: Ho bokella lintlha tse tala ho tsoa mehloling e fapaneng. 2. Tsebetso ea data esale pele: Ho hloekisa, ho hlophisa le ho fetola data ho netefatsa hore e ka sebelisoa ka nepo bakeng sa ho ithuta mochini. 3. Boenjineri ba likarolo: Ho khetha le ho lokisa likarolo tse loketseng tse emelang data hantle bakeng sa ho aha mohlala. 4. Khetho ea mohlala: Ho khetha algorithm e nepahetseng ea ho ithuta mochine e loketseng bothata bo itseng. 5. Koetliso ea mohlala: Koetliso ea algorithm e khethiloeng ho data e entsoeng esale pele le e entsoeng. 6. Netefatso ea mohlala: Ho netefatsa mohlala o koetlisitsoeng ka sete ea data eo e e-so bone pele ho lekanya tšebetso ea eona. 7. Hyperparameter tuning: Ho lokisa maemo a algorithm hantle ho ntlafatsa tsebetso ea eona. 8. Tlhahlobo ea mohlala: Ho lekola tsebetso ea mohlala ho sebelisoa metrics e nepahetseng. 9. Tšebeliso ea mohlala: Ho tsamaisa mohlala bakeng sa tšebeliso, ebang ke ka ho o kopanya tsamaisong e teng kapa ho etsa hore e fumanehe ka API. 10. Tlhokomelo le tlhokomelo ea mohlala: Ho tsoela pele ho beha mohlala leihlo le ho ntlafatsa ho netefatsa hore o lula o sebetsa le ho fana ka likhakanyo tse nepahetseng. 