AI Predictive Techniques: Discovering Data Through Machine Learning and Deep Learning

P.S., Rajeswari (Author)/ V, Vinoth Balaji (Author)

ISBN 10: 999932259X / ISBN 13: 9789999322591

Published by Eliva Press, 2024

About this Item

218 pages. 6.00x0.50x9.00 inches. In Stock.

Seller Inventory # x-999932259X

Bibliographic Details

• Title: AI Predictive Techniques: Discovering Data Through Machine Learning and Deep Learning
• Publisher: Eliva Press
• Publication Date: 2024
• Language: English
• ISBN 10: 999932259X
• ISBN 13: 9789999322591
• Binding: Paperback
• Condition: Brand New
• Weight: 0.3 kilograms

About this title

Synopsis

This book delves into the intricate world of machine learning and deep learning, offering a comprehensive guide for both beginners and seasoned professionals. It begins with foundational concepts and techniques, such as Principal Component Analysis (PCA), Linear and Logistic Regression, and Instance-Based k Neighbors (IBk). These basics lay the groundwork for understanding more complex algorithms like Decision Stump, RepTree, Naïve Bayes, and LogitBoost, each elucidated with clear explanations and practical examples.We then journey into the realm of deep learning, exploring the architectures and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). With their respective strengths in image and sequential data processing, these models have revolutionized fields ranging from computer vision to natural language processing. Practical examples using tools like Weka and Python (with Keras) provide hands-on experience, making complex concepts accessible and actionable.Real-world applications across various sectors, including healthcare, finance, retail, autonomous vehicles, and cybersecurity, illustrate the transformative impact of these technologies. Predictive analytics, fraud detection, recommendation systems, and smart grid management are just a few of the innovations driven by machine learning and deep learning.Best practices emphasize the importance of data quality, feature engineering, model selection, regularization, and ethical considerations. Continuous monitoring and updating of models are crucial to maintaining their relevance and accuracy. Looking ahead, the book discusses future trends like Explainable AI, Federated Learning, and Quantum Computing, highlighting the evolving landscape of these technologies.The book concludes with additional resources and a glossary of key terms, ensuring readers have the tools and knowledge to continue their journey in machine learning and deep learning. This comprehensive guide balances theoretical insights with practical applications, making it an invaluable resource for anyone looking to harness the power of these cutting-edge technologies.