Dimensionality Reduction in Machine Learning

Dimensionality Reduction in Machine Learning is a clear, authoritative guide from Jamal Amani Rad, Snehashish Chakraverty, and Kourosh Parand that transforms how practitioners and students handle high-dimensional data. Whether you work in computer vision, bioinformatics, finance, or IoT, this book delivers practical insight into making complex datasets tractable and model-ready.

Start with a compelling overview of why dimensionality reduction matters for modern machine learning pipelines, then move through mathematically grounded yet accessible explanations of core techniques—PCA, LDA, manifold learning, t-SNE, UMAP—and contemporary approaches for feature embedding and selection. Each chapter balances theory, intuition, and comparative analysis so you learn not just how algorithms work, but when and why to use them.

You’ll find real-world examples and clear visualizations that illustrate performance trade-offs, computational costs, and best practices for preprocessing, model integration, and evaluation. Emphasis on reproducible reasoning and hands-on problem solving makes this an ideal resource for data scientists, ML engineers, researchers, and graduate students across North America, Europe, Asia-Pacific, and beyond.

Compact yet comprehensive, this book helps you reduce noise, accelerate training, and unlock actionable patterns in large datasets. Add Dimensionality Reduction in Machine Learning to your professional library to sharpen your modeling toolkit and improve predictive performance on real-world tasks. Purchase now to advance your mastery of high-dimensional data.

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