<prompt> Hlalosa hore na phapang ke efe pakeng tsa mohlala o tebileng oa ho ithuta le mokhoa o tloaelehileng oa ho ithuta mochine.nan  <response>: Instruction in English: Explain what is the difference between a deep learning model and a traditional machine learning model. . Response in English: Machine learning and deep learning are two subsets of artificial intelligence, and the difference between them lies in the approach for solving problems, the architecture of model building, and their performance in handling complex data.Traditional machine learning or classical machine learning involves using statistical techniques and algorithms to make computers learn from data, and make predictions or decisions without being explicitly programmed to perform the task. These techniques require a lot of pre-processing of data, feature selection, and feature engineering to ensure the model performs well. Some of the well-known traditional machine learning algorithms include linear regression, random forests, support vector machines, among others.On the other hand, deep learning is a type of machine learning that involves neural networks with multiple layers. Each layer is responsible for extracting different levels of abstraction and representations from the input data. Deep learning models are inspired by the structure and function of the brain and are designed to learn on their own, by detecting the important features directly from the raw input data, eliminating the need for feature engineering. These models have the capability of handling large datasets and can deal with unstructured data like images, audio, and text, with an exceptional level of accuracy.In summary, the main differences between deep learning and traditional machine learning models include:- Data Processing: Traditional machine learning models require structured and cleaned data, while deep learning models can work with unstructured and messy data.- Feature Extraction: In traditional machine learning models, feature selection is manual, while deep learning models automatically extract relevant features from the data.- Performance: With small datasets, traditional machine learning models may outperform deep learning models. However, as the size of the dataset increases, deep learning models tend to significantly outperform traditional machine learning models.- Model Complexity: Deep learning models are typically more complex and computationally intensive when compared to traditional machine learning models, which can be relatively simple and easier to interpret. . Response in Sesotho: Ho ithuta ka mochini le ho ithuta ka botebo ke likaroloana tse peli tsa bohlale ba maiketsetso, me phapang lipakeng tsa tsona e tseleng ea ho rarolla mathata, meralo ea moaho oa mohlala, le tsebetso ea bona ea ho sebetsana le data e rarahaneng. Ho ithuta ka mochini o tloaelehileng kapa ho ithuta ka mochini oa khale ho kenyelletsa ho sebelisa mekhoa ea lipalo-palo le li-algorithms ho etsa hore likhomphutha li ithute ho tsoa ho data, le ho etsa likhakanyo kapa liqeto ntle le ho hlophisoa ka mokhoa o hlakileng ho etsa mosebetsi. Mekhoa ena e hloka ho lokisoa ha data pele ho nako, khetho ea likarolo, le boenjiniere ba likarolo ho netefatsa hore mohlala o sebetsa hantle. Tse ling tsa li-algorithms tse tsebahalang tsa ho ithuta tsa mochini li kenyelletsa ho theoha ha mela, meru e sa reroang, mechini ea li-vector ea tšehetso, hara tse ling. Ka lehlakoreng le leng, ho ithuta ho tebileng ke mofuta oa ho ithuta ka mochini o kenyelletsang marang-rang a neural a nang le mekhahlelo e mengata. Karolo e ngoe le e ngoe e na le boikarabello ba ho ntša maemo a fapaneng a ho qoelisoa le litšoantšiso ho tsoa ho data e kentsoeng. Mehlala ea ho ithuta e tebileng e bululetsoe ke sebopeho le tsebetso ea boko me e etselitsoe ho ithuta ka bobona, ka ho lemoha likarolo tsa bohlokoa ka ho toba ho data e tala ea ho kenya, ho felisa tlhokahalo ea boenjiniere ba likarolo. Mefuta ena e na le bokhoni ba ho sebetsana le li-dataset tse kholo me e ka sebetsana le boitsebiso bo sa hlophisoang joaloka litšoantšo, molumo, le mongolo, ka boemo bo ikhethang ba ho nepahala. Ka bokhutšoanyane, liphapang tse kholo lipakeng tsa thuto e tebileng le mefuta e tloaelehileng ea ho ithuta ka mochini e kenyelletsa: - Tsebetso ea Boitsebiso: Meetso ea khale ea ho ithuta ea mochini e hloka data e hlophisitsoeng le e hloekisitsoeng, athe mefuta e tebileng ea ho ithuta e ka sebetsa ka data e sa hlophisehang le e bohlasoa. - Phatlalatso ea Likarolo: Mehlala ea ho ithuta ea mochini oa setso, khetho ea likarolo e etsoa ka letsoho, athe mefuta e tebileng ea ho ithuta e ntša ka bo eona likarolo tse amehang ho data. - Tsebetso: Ka li-database tse nyane, mefuta ea khale ea ho ithuta ea mochini e ka feta mefuta e tebileng ea ho ithuta. Leha ho le joalo, ha boholo ba dataset bo ntse bo eketseha, mekhoa ea ho ithuta e tebileng e atisa ho feta mekhoa e tloaelehileng ea ho ithuta mochine. - Ho rarahana ha Mohlala: Mehlala ea ho ithuta e tebileng hangata e rarahane ebile e matla haholo ha e bapisoa le mefuta ea khale ea ho ithuta ea mochini, e batlang e le bonolo ebile e bonolo ho e hlalosa. 