Data Mining: Practical Machine Learning Tools and Techniques - Image 1

£57.89

Morgan Kaufmann Data Mining: Practical Machine Learning Tools and Techniques

Name: Data Mining: Practical Machine Learning Tools and Techniques
Brand: Morgan Kaufmann
Price: 57.89 GBP
Availability: InStock

9780443158889

Data Mining

About as cheap as it gets. The only time it was cheaper was 1 month ago.

£58 today · all-time low £56 (May 2026) · usually £58

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 74 days • 74 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 74 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £57 (49 days, 66.2%)

Price range: £56 - £58

Price levels: 3 different prices over 74 days

Description

**2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner** 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 Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Features in-depth information on deep learning and probabilistic models Covers performance improvement techniques, including input preprocessing and combining output from different methods Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software Includes all-new exercises for each chapter

Product Specifications

Brand: Morgan Kaufmann
Model: 9780443158889
Format: paperback
ASIN: 0443158886
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 10 May 2025
Listed Since: 19 July 2024

Barcode

No barcode data available

Morgan Kaufmann Data Mining: Practical Machine Learning Tools and Techniques

About as cheap as it gets. The only time it was cheaper was 1 month ago.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Meta-Learning in Computational Intelligence: 358

Data Science Concepts and Techniques with Applications

Applied Data Analytics - Principles and Applications (River Publishers Series in Signal, Image and Speech Processing)

Feature Selection for Data and Pattern Recognition: 584 (Studies in Computational Intelligence, 584)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R

Springer Feature Selection for Data and Pattern Recognition 584

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III: 11441 (Lecture Notes in Computer Science, 11441)

Machine Learning and Artificial Intelligence: Concepts, Algorithms and Models

Machine Learning and Knowledge Discovery in Databases, Part II: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, ... (Lecture Notes in Computer Science, 6912)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Data Mining: A Tutorial-Based Primer, Second Edition (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Data Mining With Decision Trees: Theory And Applications (2Nd Edition): 81 (Series In Machine Perception And Artificial Intelligence)

Neural Networks and Deep Learning: A Textbook

Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach

International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020): Emerging Applications and Technologies for Industry ... (Lecture Notes in Networks and Systems, 254)

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

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures

Meta-Learning in Decision Tree Induction: 498 (Studies in Computational Intelligence, 498)

Advances in Machine Learning for Big Data Analysis: 218 (Intelligent Systems Reference Library, 218)