Statistical Modeling in Machine Learning 1st Edition

Statistical Modeling in Machine Learning (1st Edition) by Tilottama Goswami and G. R. Sinha is a clear, contemporary guide that bridges rigorous statistical theory and modern machine learning practice. Whether you’re a graduate student, data scientist, or research professional, this volume brings clarity to complex concepts and shows how statistical thinking strengthens predictive modeling.

Start with a strong foundation in probability, inference, and model selection, then progress to applied techniques used across industries—regression, generalized linear models, Bayesian approaches, and regularization methods—presented with practical intuition and real-world relevance. The authors emphasize interpretability, robust evaluation, and the interplay between assumptions and algorithmic performance, making the book ideal for anyone transforming data into reliable insight.

Readers gain immediate value from accessible explanations, illustrative examples, and guided problem-solving that connect classroom theory to production-ready workflows. The text is especially useful for those preparing for research, competitive data roles, or interdisciplinary projects in finance, healthcare, engineering, and beyond.

Globally relevant and pedagogy-focused, this edition caters to an international audience—from North America and Europe to Asia and emerging tech hubs—providing tools that translate across data environments and regulatory contexts. Concise, authoritative, and practical, it’s a reference you’ll return to as your projects scale.

Unlock stronger models and clearer decisions—add Statistical Modeling in Machine Learning to your library today and elevate how you design, evaluate, and communicate machine learning solutions. Order now to start applying statistically sound methods to real-world problems.

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