Christopher J. Pal, Eibe Frank, Ian H. Witten, James Foulds, Mark A. Hall

Data Mining

Practical Machine Learning Tools and Techniques

• Broschiertes Buch

Produktbeschreibung

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Technology
• Seitenzahl: 688
• Erscheinungstermin: 1. März 2025
• Englisch
• Abmessung: 235mm x 191mm
• Gewicht: 450g
• ISBN-13: 9780443158889
• ISBN-10: 0443158886
• Artikelnr.: 72175772

Inhaltsangabe

PART I: INTRODUCTION TO DATA MINING 1. What’s it all about? 2. Input:
concepts, instances, attributes 3. Output: knowledge representation 4.
Algorithms: the basic methods 5. Credibility: evaluating what’s been
learned 6. Preparation: data preprocessing and exploratory data analysis 7.
Ethics: what are the impacts of what's been learned? PART II: MORE ADVANCED
MACHINE LEARNING SCHEMES 8. Ensemble learning 9. Extending instance-based
and linear models 10. Deep learning: fundamentals 11. Advanced deep
learning methods 12. Beyond supervised and unsupervised learning 13.
Probabilistic methods: fundamentals 14. Advanced probabilistic methods 15.
Moving on: applications and their consequences