<prompt> Etsa lethathamo la lipotso ho hlahloba tsebo ea mosebelisi ka mekhoa ea ho ithuta ka mochini.nan  <response>: Instruction in English: Create a list of questions to assess a users familiarity with machine learning techniques. . Response in English: Here is a list of ten questions that can be used to assess a users familiarity with machine learning techniques:1. Can you explain the difference between supervised and unsupervised machine learning?2. Which are some of the most common metrics used to evaluate the performance of a machine learning model?3. How do you handle missing data when preprocessing for machine learning?4. Can you explain the difference between overfitting and underfitting in machine learning?5. Can you describe an example of how the bias-variance tradeoff can affect a machine learning models performance?6. Can you explain the concept of gradient descent and its role in training machine learning models?7. How do you select the number of clusters when using k-means clustering?8. Can you give an example of how regularization can help prevent overfitting in a machine learning model?9. Can you describe the role of decision trees in machine learning methods?10. Which approach would you use to address the class imbalance problem in a dataset when training a machine learning model? . Response in Sesotho: Mona ke lethathamo la lipotso tse leshome tse ka sebelisoang ho lekola tsebo ea mosebelisi ka mekhoa ea ho ithuta ka mochini: 1. Na u ka hlalosa phapang lipakeng tsa thuto ea mochini e hlokometsoeng le e sa laoleheng? 2. Tse ling tsa metrics tse sebelisoang haholo ho lekola tšebetso ea mochini oa ho ithuta ke life? 3. U sebetsana joang le data e sieo ha u ntse u tsoela pele ho ithuta mochine? 4. Na u ka hlalosa phapang pakeng tsa ho overfitting le underfitting thutong ea mochini? 5. Na u ka hlalosa mohlala oa kamoo khoebo ea bias-variance tradeoff e ka amang tsebetso ea mochini oa ho ithuta? 6. Na o ka hlalosa mohopolo oa ho theoha ha gradient le karolo ea ona ho koetlisa mehlala ea ho ithuta ka mochini? 7. U khetha palo ea lihlopha joang ha u sebelisa k-means clustering? 8. A na u ka fana ka mohlala oa kamoo ho tloaeleha ho ka thusang ho thibela ho feta tekano ka mokhoa oa ho ithuta mochine? 9. Na o ka hlalosa karolo ea lifate tsa liqeto mekhoeng ea ho ithuta ka mochini? 10. Ke mokhoa ofe oo u ka o sebelisang ho rarolla bothata ba ho se leka-lekane ha sehlopha ka hara dataset ha u koetlisa mohlala oa ho ithuta ka mochini? 