Handbook of Research on Machine Learning 1st Edition

Capture the cutting edge of artificial intelligence with Handbook of Research on Machine Learning, 1st Edition by Monika Mangla, PhD. This authoritative guide commands attention with a clear, research-driven approach to contemporary machine learning techniques and their real-world impact.

Dive into rigorously organized chapters that bridge theory and practice: from supervised and unsupervised learning to deep learning architectures, reinforcement learning, feature engineering, model evaluation, and explainable AI. Each section emphasizes methodological clarity, reproducible experiments, and scalable solutions for large datasets—making complex algorithms accessible to both academic researchers and industry practitioners.

Whether you’re a graduate student, data scientist, AI engineer, or university faculty, this handbook accelerates research productivity. Learn how to design robust experiments, optimize model performance, and translate findings into deployable systems. Practical perspectives on ethical considerations, interpretability, and deployment challenges equip readers to meet regulatory and societal expectations in regions spanning North America, Europe, Asia, Africa, and Australia.

What sets this edition apart is its balance of mathematical rigor and application-driven insight—ideal for course adoption, lab reference, or professional development. Rich with examples, comparative analyses, and best-practice recommendations, it serves as a go-to resource for those looking to publish, teach, or build machine learning solutions that scale.

Add Handbook of Research on Machine Learning to your professional library and strengthen your command of AI research and implementation. Order your copy today to stay at the forefront of machine learning innovation and advance your projects with trusted, scholarly guidance.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.