Advanced Data Science and Analytics with Python Advanced Data Science and Analytics with Python
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Advanced Data Science and Analytics with Python

    • US$72.99
    • US$72.99

출판사 설명

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.

The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.

Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today’s AI-driven world.

Jesús Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

장르
비즈니스 및 개인 금융
출시일
2026년
8월 26일
언어
EN
영어
길이
632
페이지
출판사
CRC Press
판매자
Taylor & Francis Group
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34.6
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