Machine Learning: Algorithms, Models, and Applications
It serves as a resource for students, researchers, and professionals in ML/AI, but is incremental as it compiles existing applications without introducing new methods.
The paper presents a collection of innovative research works applying machine learning and deep learning algorithms to real-world domains like stock trading, healthcare, and software automation, focusing on their design, optimization, and deployment.
Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.