<prompt> Hlalosa mekhahlelo e fapaneng ea tsebetso ea ho ithuta ka mochini.nan  <response>: Instruction in English: Explain the different phases of a machine learning workflow. . Response in English: Machine learning workflow describes the process of building and deploying machine learning models. This process comprises several phases, including data collection, data preprocessing, model selection, training, evaluation, and deployment.1. Data Collection: The first step in the machine learning workflow is to collect data to train a model. Data can be retrieved from different sources such as databases, spreadsheets, APIs, or web scraping.2. Data Preprocessing: After data collection, we have to preprocess the data to make it ready for training. This phase involves data cleaning, handling missing or incorrect data, transforming variables, data encoding, and data normalization.3. Model Selection: Once the data is preprocessed, the next step is to choose an appropriate machine learning algorithm for the problem. This depends on factors such as the type and size of the data, the problem being solved, and performance requirements.4. Training: The selected algorithm is then trained on the preprocessed data. The machine learning model learns from the data by optimizing the parameters to minimize the errors between the predicted and actual values.5. Evaluation: After the training phase, the model needs to be evaluated to assess its performance. We use evaluation metrics such as accuracy, precision, recall, AUC-ROC, or confusion matrix, depending on the problem and the model. If the performance is not satisfactory, the model selection and training phases may need to be repeated until a suitable model is found.6. Deployment: Once the model is trained and evaluated, it can be deployed to production, where it can be used to make predictions on new data. The deployed model will need to be monitored to ensure that it is still performing accurately and may need to be retrained or updated if theres new data or if its performance decreases over time. . Response in Sesotho: Mosebetsi oa ho ithuta ka mochini o hlalosa mokhoa oa ho aha le ho tsamaisa mefuta ea ho ithuta ka mochini. Tsebetso ena e na le mekhahlelo e mengata, ho kenyelletsa pokello ea data, ho lokisa data esale pele, khetho ea mohlala, koetliso, tlhahlobo le phepelo. 1. Pokello ea Lintlha: Mohato oa pele mosebetsing oa ho ithuta mochine ke ho bokella lintlha ho koetlisa mohlala. Lintlha li ka fumanoa mehloling e fapaneng e kang databases, spreadsheets, APIs, kapa web scraping. 2. Phatlalatso ea Boitsebiso: Ka mora ho bokella lintlha, re tlameha ho lokisa lintlha pele ho li lokisetsa koetliso. Karolo ena e kenyelletsa tlhoekiso ea data, ho sebetsana le data e sieo kapa e fosahetseng, ho fetola mefuta e fapaneng, khouto ea data, le ho tloaeleha ha data. 3. Khetho ea Mohlala: Hang ha data e se e hlophisitsoe, mohato o latelang ke ho khetha algorithm e nepahetseng ea ho ithuta mochine bakeng sa bothata. Sena se ipapisitse le lintlha tse kang mofuta le boholo ba data, bothata bo rarolloang, le litlhoko tsa tsebetso. 4. Koetliso: Algorithm e khethiloeng e ntano koetlisetsoa ho data e entsoeng esale pele. Moetso oa ho ithuta ka mochini o ithuta ho tsoa ho data ka ho ntlafatsa liparamente ho fokotsa liphoso lipakeng tsa boleng bo boletsoeng esale pele le ba nnete. 5. Tlhahlobo: Ka mora mohato oa koetliso, mohlala o hloka ho hlahlojoa ho hlahloba tsebetso ea oona. Re sebelisa metrics ea tlhahlobo e kang ho nepahala, ho nepahala, ho hopola, AUC-ROC, kapa matrix a pherekano, ho itšetlehile ka bothata le mohlala. Haeba tsebetso e sa khotsofatse, khetho ea mohlala le mekhahlelo ea koetliso e ka na ea hloka ho phetoa ho fihlela ho fumanoa mohlala o loketseng. 6. Ho tsamaisoa: Hang ha mohlala o koetlisitsoe le ho hlahlojoa, o ka fetisetsoa tlhahiso, moo o ka sebelisetsoang ho bolela esale pele ka data e ncha. Moetso o rometsoeng o tla hloka ho beoa leihlo ho netefatsa hore o ntse o sebetsa ka nepo mme o kanna oa hloka ho koetlisoa kapa ho nchafatsoa haeba ho na le data e ncha kapa tsebetso ea ona e fokotseha ha nako e ntse e ea. 