Machine Learning Data Analytics Predicting Managing Monitoring Disease IGI 2021

FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTICS

Algorithms, Worked Examples, and Case Studies

Authors: John D. Kelleher, Brian Mac Namee, Aoife D\'Arcy

Publisher: MIT Press

Publication Year: 2015

Format: Hardcover

Pages: 624

ISBN-13: 9780262029445

Language: English

Condition: VERY GOOD

CONDITION:

Pre-owned hardcover textbook in VERY GOOD condition. Please review all photographs for the exact cosmetic condition of the book.

ABOUT THE BOOK:

An authoritative, comprehensive introduction to machine learning and predictive data analytics from MIT Press.

This 624-page textbook explains the fundamental algorithms, mathematical concepts, and practical applications behind machine learning. It combines technical explanations with step-by-step worked examples and real-world case studies.

TOPICS COVERED:

- Machine Learning Fundamentals

- Predictive Data Analytics

- Data Exploration and Preparation

- Information-Based Learning

- Similarity-Based Learning

- Probability-Based Learning

- Error-Based Learning

- Statistical Modeling

- Predictive Model Evaluation

- Customer Churn Prediction

- Galaxy Classification

- Real-World Business Applications

WHY BUY THIS BOOK?

- Published by the prestigious MIT Press

- Comprehensive 624-page hardcover reference

- Detailed algorithms and worked examples

- Practical case studies demonstrating real applications

- Excellent resource for computer science, mathematics, statistics, engineering, and data science students

- Valuable reference for programmers, researchers, data analysts, and machine learning professionals

- Original hardcover edition published in 2015

Whether you\'re studying machine learning academically, developing predictive models, or expanding your professional technical library, this textbook provides a strong foundation in the principles and practical applications of predictive analytics.

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