Abstract
The trend in the current industry and academics is machine learning (ML), and it is not hype but the reality. The market requirements are not limited by the earlier computing and storage models if those can be integrated with ML algorithms. The requirements of current industry, research and other domains like banking, finance, retail and medical have to depend on the collection of huge amounts of the data and analyse the data to better serve the stakeholders. The organizations have to focus on better storage models and advance logics of ML to meet the current needs of the people and usage of the limited amount of time to handle the huge amounts of the data. The ML research is moving in a high potential with the involvement of the social media like Facebook, Twitter and Google. These are not only using the ML perspective in their applications but also contributing to the development of new algorithms and API in the context of big data, ML and deep learning. The paper objective is to walk through the ML, big data along with deep learning fundamentals with the specification of various algorithms in the reference of above-mentioned social media with their contributions in the development of ML landscape along with the API (TensorFlow), services like priority inbox and the algorithms like RankBrain. The discussion will help the researchers and academicians so as to get the overview and detailed significance of ML research and the importance of ML in various dimensions. All these algorithms, tools and services are indirectly dependent on the artificial intelligence (AI) to frame the rules and to fine-tune the rules as per the requirements.
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Umapavankumar, K., Srinivasu, S.V.N., Siva Nageswara Rao, S., Thirumala Rao, S.N. (2020). Machine Learning Usage in Facebook, Twitter and Google Along with the Other Tools. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_43
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DOI: https://doi.org/10.1007/978-981-15-0135-7_43
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Online ISBN: 978-981-15-0135-7
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