Data Mining
Practical Machine Learning Tools and Techniques
- Publisher's listprice EUR 69.95
-
29 672 Ft (28 259 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 5 934 Ft off)
- Discounted price 23 738 Ft (22 607 Ft + 5% VAT)
29 672 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 5
- Publisher Morgan Kaufmann
- Date of Publication 1 April 2025
- ISBN 9780443158889
- Binding Paperback
- No. of pages688 pages
- Size 234x190 mm
- Weight 1550 g
- Language English 0
Categories
Long description:
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
Table of Contents:
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
Data Mining: Practical Machine Learning Tools and Techniques
Subcribe now and receive a favourable price.
Subscribe
29 672 HUF
