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singh p data analytics machine learning navigating big data landscape 2024

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Textbook in PDF format This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, Big Data, and Machine Learning solutions in their own organizations. The book discusses the transformative power of data analytics and Big Data in various industries and sectors and how Machine Learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how Big Data explosion, the power of analytics and Machine Learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, Big Data, and Machine Learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data. Data has become the main driver behind innovation, decision-making, and the change of many sectors and civilisations in the modern period. The dynamic trinity of Data Analytics, Big Data, and Machine Learning is thoroughly introduced in this chapter, which also reveals their profound significance, intricate relationships, and transformational abilities. The fundamental layer of data processing is data analytics. Data must be carefully examined, cleaned, transformed, and modelled in order to reveal patterns, trends, and insightful information. A data-driven revolution is sparked by big data. In our highly linked world, data is produced in enormous numbers, diversity, velocity, and authenticity. The third pillar, machine learning, uses data-driven algorithms to enable automated prediction and decision-making. This chapter explores the key methods and equipment needed to fully utilise the power of data analytics and also discusses how technologies used in Big Data management, processing, and insight extraction. A foundation is set for a thorough investigation of these interconnected realms when we begin the chapters that follow. Data analytics, Big Data, and Machine Learning are not distinct ideas; rather, they are woven into the fabric of modern innovation and technology. This chapter serves as the beginning of this captivating journey, providing a solid understanding of and insight into the enormous possibilities of data-driven insights and wise decision-making. Hence in this paper a predictive model using machine learning-based linear regression algorithm is used. The study is done using a popular statistical tool named R programming. In this study the R tool is explored and represented with utility for recommendation system designing and finding insights from data by showing various plots. The results are formulated and presented in a formal and structured way using the R tool. During this study it has been observed that the R tool has potential to be one of the leading tools for research and business analytics. Contents: Introduction to Data Analytics, Big Data, and Machine Learning Fundamentals of Data Analytics and Lifecycle Building Predictive Models with Machine Learning Predictive Algorithms for Smart Agriculture Stream Data Model and Architecture Leveraging Data Analytics and a Deep Learning Framework for Advancements in Image Super-Resolution Techniques: From Classic Interpolation to Cutting-Edge Approaches Applying Data Analytics and Time Series Forecasting for Thorough Ethereum Price Prediction Practical Implementation of Machine Learning Techniques and Data Analytics Using R Deep Learning Techniques in Big Data Analytics Data Privacy and Ethics in Data Analytics Modern Real-World Applications Using Data Analytics and Machine Learning Real-World Applications of Data Analytics, Big Data, and Machine Learning Unlocking Insights: Exploring Data Analytics and AI Tool Performance Across Industries Lung Nodule Segmentation Using Machine Learning and Deep Learning Techniques Convergence of Data Analytics, Big Data, and Machine Learning: Applications, Challenges, and Future Direction Business Transformation Using Big Data Analytics and Machine Learning

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Singh P. Data Analytics and Machine Learning.Navigating..Big Data Landscape 2024.pdf 10.7 MB

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singh p data analytics machine learning navigating big data landscape 2024

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