MACHINE LEARNING: APPLICATION AND CHALLENGES

Prateek Agrawal · Dr. Nilesh Marathe · Dr. Haewon Byeon · Mr. Sandip Kumar Singh

aug. 2024 · Xoffencer international book publication house

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Om denne e-boken

Machine learning, often known as ML, has brought about a revolution in a variety of industries by empowering computers to recognize patterns and draw conclusions from data without the need for explicit programming. Applications of this technology include a wide range of domains, including healthcare, where it is used to assist in the diagnosis of illnesses, the prediction of patient outcomes, and the customization of treatment programs. ML models improve the identification of fraudulent activity, algorithmic trading, and risk assessment in the financial sector. In addition, the technology is used to power recommendation systems in the entertainment and e-commerce industries, which serve to optimize user experiences by anticipating preferences. When it comes to autonomous cars, machine learning algorithms evaluate enormous volumes of sensor data in order to navigate and make judgments in real time. The application of machine learning, on the other hand, confronts substantial hurdles. Both the quality and amount of the data are very important; faulty models might be the result of lacking or biased data. An additional challenge is ensuring that complicated models are both transparent and interpretable. This is particularly important in key applications such as healthcare and finance, where it is essential to have a solid grasp of decision-making processes. There are also worries over privacy that occur as a result of the enormous data collecting that is necessary, which calls for stringent data security measures. In addition, the incorporation of machine learning systems into preexisting infrastructures may be a difficult and expensive process, requiring a significant amount of computing resources and a high level of knowledge. The continual breakthroughs in machine learning research and technology continue to increase its potential and application, providing creative solutions across a variety of areas, altering industries, and solving complex global concerns. This is despite the hurdles that have been presented. The continual breakthroughs in machine learning research and technology continue to increase its potential and application, providing creative solutions across a variety of areas, altering industries, and solving complex global concerns. This is despite the hurdles that have been presented. In the field of climate science, for instance, machine learning is used to model and forecast weather patterns, monitor deforestation, and maximize the utilization of renewable energy sources. The use of precision farming methods, the prediction of yield outcomes, and the monitoring of plant health are all ways in which it improves crop management with regard to agriculture.

Om forfatteren

Prateek Agrawal is an accomplished IT professional with a Master's degree in Computer Applications from Gurukul Kangri University. With 23 years of industry experience working for renowned companies like IBM, Capgemini, Amazon, and Fidelity Investments, he has garnered extensive expertise in the field. Prateek's recent interests revolve around the adoption of generative AI technologies in various industries and their applications in solving customer business problems. He possesses in-depth knowledge of Amazon's generative AI technologies, such as Bedrock, SageMaker, and Amazon Q. Prateek has achieved the prestigious Machine Learning Specialty certification from AWS, further solidifying his expertise in the field. As a published author of five articles in the realms of data science and AI technologies, he has made significant contributions to the industry. Passionate about mentoring the next generation of architects, Prateek is dedicated to sharing his knowledge and insights with aspiring professionals.

Dr. Nilesh Marathe is an Associate Professor in the Computer Science and Engineering department, specializing in Data Science, at SVKM’s Dwarkadas J. Sanghvi College of Engineering (DJSCE) in Vile Parle, Mumbai. He joined the institution on December 16, 2022, and holds a Ph.D. in Computer Engineering from Mumbai University (2020), an M.E. in Computer Engineering (2011), and a B.E. in Computer Science and Engineering (2001), both from Mumbai University. He also holds a Diploma in Computer Technology from MSBTE (1998). With over 20 years of teaching experience and 2 years of industrial experience, Dr. Marathe has held various academic positions, including Associate Professor at MIT Art, Design and Technology University and Assistant Professor at Ramrao Adik Institute of Technology. He has taught a variety of courses at both undergraduate and postgraduate levels, covering areas such as E-commerce, Computer Networks, Network Security, and Mobile Computing.

Dr. Haewon Byeon received the Dr. degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

Mr. Sandip Kumar Singh is an esteemed Assistant Professor in the Department of Information Technology at RR Group of Institutions, Lucknow. With a passion for technology and education, Mr. Singh has carved a niche for himself in the academic world, particularly in the rapidly evolving fields of Machine Learning and Blockchain Technology. His expertise in Machine Learning is underpinned by a thorough understanding of algorithms, data structures, and the mathematical foundations that drive this cutting-edge field. Machine Learning, a subset of artificial intelligence, has revolutionized the way we approach problem-solving in numerous domains, and Mr. Singh's work in this area is a testament to his dedication to advancing both theoretical and practical knowledge. As an Assistant Professor, Mr. Singh is not only dedicated to his own research but also to the education and development of his students. He believes in fostering a learning environment that encourages curiosity, critical thinking, and innovation. His teaching philosophy revolves around bridging the gap between theoretical concepts and realworld applications, ensuring that his students are well-equipped to tackle the challenges of the ever-changing technological landscape.

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