Textbook in PDF format

Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations.
Machine Learning Applications discusses methodological advancements of Machine Learning and Deep Learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning.
In our rapidly evolving world, the transformative power of Machine Learning (ML) and Deep Learning (DL) technologies is undeniable. From robotics and vehicle automation to financial services, retail, manufacturing, healthcare, and beyond, ML and DL are revolutionizing industries and driving improvements in business operations. The potential of these advanced technologies to enhance our lives and reshape our future is immense.
In this book, we delve into the remarkable advancements made possible by ML and DL, showcasing case studies that demonstrate how these technologies have facilitated breakthroughs in business intelligence, enabling faster and more efficient decision-making processes. We explore a wide range of applications, from facial recognition to natural language processing, and illustrate how ML and DL play a central role in the continuous learning and data simulation capabilities of cars in real-time.
Within these pages, we uncover the inner workings of ML algorithms, revealing how they transform digital images, which are mere series of numbers, into meaningful patterns through image processing techniques. We also explore the complex landscapes of risk modeling, genomic sequencing, and modeling, where ML and DL implementations require extensive cloud environments with high-performance data processing and management capabilities.
Moreover, we examine the competitive landscape of ML- and DL-based platforms, where major vendors such as Amazon, Google, Microsoft, IBM, and others vie for customers by offering comprehensive services encompassing data collection, classification, modeling, training, and application deployment.
Composed of 13 chapters, this book also introduces real-world applications of machine and Deep Learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective.
Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on:
Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records
With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as Machine Learning and Deep Learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more