<prompt> Etsa lethathamo la meeli ea li-algorithms tsa ho ithuta ka mochini?nan  <response>: Instruction in English: List the limitations of machine learning algorithms? . Response in English: Machine learning algorithms have several limitations, including:1. Data Quality: Machine learning algorithms are only as good as the data they are trained on. If the data is of poor quality, biased, inconsistent or contains errors, the model will learn from those errors and produce inaccurate predictions.2. Generalization: Sometimes machine learning models are trained on specific datasets and may not perform well when encountering new data or scenarios. This could be due to overfitting, where the model is too complex and performs well on the training data, but cannot generalize to new data.3. Interpretability: Many machine learning models can be difficult to interpret and understand, particularly complex models such as deep neural networks. This can make it difficult to explain how the model makes its predictions and can limit its usefulness in certain applications.4. Bias: Machine learning algorithms can be biased if the training data is biased. This can result in discriminatory outcomes and perpetuate existing biases in the data.5. Need for Large Amounts of Data: Machine learning algorithms often require large amounts of data to produce accurate results. This can be a limitation for applications with small or limited datasets.6. Resource Intensive: Training machine learning models can be resource-intensive, requiring significant computing power and storage. This can be a limitation for certain applications or organizations.7. Requires human expertise: While machine learning is often portrayed as a fully autonomous process, it in fact requires human expertise to design and fine-tune the model, select the right algorithm, and interpret the results. . Response in Sesotho: Mekhoa ea ho ithuta ka mochini e na le mefokolo e mengata, ho kenyelletsa le: 1. Boleng ba data: Litaelo tsa ho ithuta ka mochini li ntle joalo ka data eo li koetliselitsoeng ho tsona. Haeba data e le ea boleng bo tlaase, e leeme, e sa lumellane kapa e na le liphoso, mohlala o tla ithuta liphosong tseo me o hlahise likhakanyo tse sa nepahalang. 2. Kakaretso: Ka linako tse ling mefuta ea ho ithuta ka mochini e koetlisetsoa ho li-dataset tse ikhethileng me e kanna ea se sebetse hantle ha e kopana le data kapa maemo a macha. Sena se ka bakoa ke ho fetella, moo mohlala o rarahaneng haholo me o sebetsa hantle ho data ea koetliso, empa o ke ke oa akaretsa lintlha tse ncha. 3. Tlhaloso: Mefuta e mengata ea ho ithuta ka mochini e ka ba thata ho e hlalosa le ho e utloisisa, haholo mefuta e rarahaneng joalo ka marang-rang a tebileng a methapo ea kutlo. Sena se ka etsa hore ho be thata ho hlalosa hore na mohlala o etsa likhakanyo tsa oona joang me o ka fokotsa molemo oa oona litsebetsong tse itseng. 4. Leeme: Mekhoa ea ho ithuta ka mochine e ka ba leeme haeba lintlha tsa koetliso li leeme. Sena se ka fella ka liphello tse khethollang le ho ntšetsa pele leeme le teng ho data. 5. Tlhokahalo ea Boitsebiso bo Boholo ba Boitsebiso: Mekhoa ea ho ithuta ka mochine hangata e hloka boitsebiso bo bongata ho hlahisa liphello tse nepahetseng. Sena e ka ba moeli bakeng sa litsebetso tse nang le li-dataset tse nyane kapa tse fokolang. 6. Mehloli e Fetang: Mehlala ea ho ithuta mochini oa koetliso e ka ba matla haholo, e hlokang matla a bohlokoa a komporo le polokelo. Sena e ka ba moeli bakeng sa likopo tse itseng kapa mekhatlo. 7. E hloka tsebo ea motho: Le hoja ho ithuta ka mochine hangata ho hlalosoa e le mokhoa o ikemetseng ka ho feletseng, ha e le hantle o hloka tsebo ea motho ho qapa le ho lokisa mohlala, ho khetha algorithm e nepahetseng, le ho hlalosa liphello. 