Alsharif, Mohammed H.Kelechi, Anabi HilaryChaudhry, Shehzad Ashraf2020-05-182020-05-1820202073-8994https://hdl.handle.net/11363/2148https://doi.org/Document Information Language:English Accession Number: WOS:000516823700088Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.eninfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 United Statesmachine learningartificial intelligencesupervised learningunsupervised learningbig datainternet of thingsNAIVE BAYESSVMCLASSIFIERSMODELArticle12110.3390/sym120100882-s2.0-85083439425Q2WOS:000516823700088Q2