Sale!
Read the Book
₹1,250.00 ₹1,150.00
DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING - Image 2
DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING - Image 3
DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING - Image 4
DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING - Image 5
DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING - Image 6

DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING

Anjan Kumar Reddy Ayyadapu is a Big data Solutions Architect leading Edge to AI Platform Solutions at Cloudera. He held several roles in Accelerating Digital Transformation for world’s largest enterprises delivering Modern Innovative Solutions on hybrid data platform using the Power of Generative AI and Secure Data Management. He is a certified AI, Machine Learning Engineer from Stanford University, Google Cloud and Amazon Web Services Solution Architect with Cyber Security Administration on Private & Public clouds like AWS, GCP. Anjan earned Master of Science from University of South Alabama and Bachelor of Technology from Jawaharlal Nehru Technological University.

Niyati Bhat is an accomplished researcher specializing in Artificial Intelligence, Machine Learning, and Deep Learning. Niyati earned her Master’s in Computer Science from California State University-Long Beach in 2017, building on her foundation in computer engineering obtained in 2014 from the University of Mumbai. In recognition of her significant contributions, she received the “YOUNG RESEARCHER AWARD” in 2024 for her work in AI. Niyati has also co-authored technical papers featured in reputable international journals like IEEE, showcasing her commitment to advancing these fields.

Dr. Anil Kumar stands out as a remarkable figure in the realm of Chemistry, blending 16 years of teaching and research excellence with significant contributions to education and innovation. As Head of the Department, his leadership extends beyond academia, embracing administrative prowess and a commitment to creating a nurturing educational environment. His creation of the ACMI model demonstrates a dedicated effort to bridge theoretical chemistry with practical applications, particularly in the microbial study of complexes. Through his YouTube channel, “HOTSPOT CHEMISTRY BY DR ANIL KUMAR,” he significantly expands the reach of his expertise, democratizing education for a wider audience. Dr. Kumar’s proficiency with advanced analytical instruments and his exploration of AI and IoT signify a progressive approach to scientific research. His prolific output includes over 80 publications, authorship of pivotal texts in chemistry, and the guidance of numerous scholars, highlighting his role as a beacon of knowledge and innovation. Through his active participation in various committees and coordination roles, he has made notable contributions to the academic community, receiving national and international recognition. Dr. Kumar’s work exemplifies a synergistic blend of leadership, research, and a dedication to education, positioning him as a key figure in advancing the field of chemistry and its application in solving global challenges.

Dr. S A Sivasankari is a Assistant professor in Vignan Foundation for science technology and research. She joined in this university in the department of electronics and communication engineering. Her research and teaching continues more than one decades at various Universities and affiliated colleges. She is the author and co-author of 2 books on block chain technology and deep learning technology. As a teacher her potential is best in all electronic basic subjects. She is the author and co-author of over 30 Research papers and her contribution have been published in high impact factor journals . Her current research interests are in the areas in Low power VLSI, IoT, telecommunication, machine learning and Artificial intelligence. She holds her B.E(ECE), MTech (VLSI DESIGN), PhD from SIMATS and Post-Doctoral Fellowship from UNISINOS (BRAZIL).

 

Description

In the field of data visualization and interpretation, machine learning (ML) is an essential component in the process of gleaning insights from complicated information. Through the use of complex algorithms and methods, machine learning makes it possible to transform raw data into representations that are easily understood by the human eye. This permits a more in-depth comprehension of the patterns and trends that lie under the surface. To condense high-dimensional data into lowerdimensional spaces while maintaining critical features, one way involves utilizing dimensionality reduction techniques such as principal component analysis (PCA) or tdistributed stochastic neighbor embedding (t-SNE). These techniques are examples of dimensionality reduction techniques. It is then possible to view these simplified representations in two or three dimensions, which enables analysts to investigate the correlations between variables and locate clusters or abnormalities within the data. Visualization tools that are powered by machine learning typically include interactive components and parameters that can be customized, which gives users the ability to change and analyze data in a dynamic manner. Clustering algorithms, regression analysis, and classification models are some of the techniques that are effortlessly incorporated into visualization platforms. This allows users to get insights in real time and make decisions that are successfully driven by data. In order to facilitate the automation of the interpretation process, machine learning techniques can be utilized to extract useful insights from visualizations. Natural language processing (NLP) techniques can be applied to generate written summaries or comments based on visual representations. This provides analysts with assistance in comprehending intricate patterns and successfully presenting their findings. It is possible for academics and practitioners to discover hidden insights, identify trends, and derive actionable intelligence from big and diverse datasets by utilizing the synergy between data visualization and machine learning. This synergy offers great potential for increasing data analysis capabilities across a variety of areas.

Reviews

There are no reviews yet.

Add a review

Your email address will not be published. Required fields are marked *

Related products