Название: The Shape of dаta: Geometry-Based Machine Learning and Data Analysis in RАвтор: Colleen M. Farrelly, Yae Ulrich Gaba
Издательство: No Starch Press
Год: 2023
Страниц: 264
Язык: английский
Формат: epub (true), mobi
Размер: 11.8 MB
Whether you’re a mathematician, seasoned data scientist, or marketing professional, you’ll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and Machine Learning.
This book’s extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis.
Throughout the book’s mathematical data analytics tour, we encounter the origin of data analysis on structured data and the many seemingly unstructured data scenarios that can be turned into structured data, which enables standard machine learning algorithms to perform predictive and prescriptive analytical insights. As we ride through the valleys and peaks of our data, we learn to collect features along the way that become key inputs into other data layers, forming geometrical interpretations of varying unstructured data sources including network data, images, and text-based data. In addition, Farrelly and Gaba are masterful in detailing the foundational and advanced concepts supported by the well-defined examples in both R and Python, available for download from their book’s web page.
This book will be relevant and captivating to beginners and devoted experts alike. First-time travelers will find it easy to dive into algorithm examples designed for analyzing network data, including social and geographic networks, as well as local and global metrics, to understand network structure and the role of individuals in the network. The discussion covers clustering methods developed for use on network data, link prediction algorithms to suggest new edges in a network, and tools for understanding how, for example, processes or epidemics spread through networks.
Advanced readers will find it intriguing to dive into recently developing topics such as replacing linear algebra with nonlinear algebra in Machine Learning algorithms and exterior calculus to quantity needs in disaster planning. The Shape of Data has made me want to roll up my sleeves and dive into many new challenges, because I feel as well equipped as Lara Croft in Tomb Raider thanks to Farrelly’s tremendous treasure map and deeply insightful exploration work. Could there be a hidden bond or “hidden layer” between them?
In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you’ll explore:
• Supervised and unsupervised learning algorithms and their application to network data analysis
• The way distance metrics and dimensionality reduction impact Machine Learning
• How to visualize, embed, and analyze survey and text data with topology-based algorithms
• New approaches to computational solutions, including distributed computing and quantum algorithms
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