Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling 1st Edition

Capture the cutting edge of chemical analysis with Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling, 1st Edition by Jahan Ghasemi. This definitive guide bridges advanced data science and practical chemistry, delivering clear, actionable strategies for turning complex multivariate data into confident decisions.

Begin with accessible explanations of core concepts—multivariate statistics, feature selection, dimensionality reduction, and model validation—and progress to applied techniques such as PCA, PLS, clustering, SVM, and neural networks tailored for chemical datasets. Real-world examples from spectroscopy, chromatography, process monitoring, and materials discovery illustrate how data-driven models improve accuracy, robustness, and interpretability in both academic labs and industrial settings.

Designed for analytical chemists, chemometricians, graduate students, and R&D professionals, this book emphasizes reproducible workflows and best practices for building reliable predictive models. Whether you work in pharmaceuticals, environmental analysis, petrochemicals, or academic research, Ghasemi’s balanced mix of theory and application helps you reduce experimental costs, accelerate discovery, and scale analyses across projects.

Global in scope and locally relevant, the text supports researchers and practitioners across North America, Europe, Asia, and beyond who seek to adopt machine learning in chemistry. Clear visuals, practical algorithms, and step-by-step guidance make complex methods approachable without sacrificing rigor.

For anyone ready to harness multivariate and data-driven modeling to elevate chemical research, Machine Learning and Pattern Recognition Methods in Chemistry is an essential resource. Order your copy today to start transforming data into insight and driving smarter, faster outcomes in your lab or classroom.

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