Title
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies,New
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A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: informationbased learning, similaritybased learning, probabilitybased learning, and errorbased learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Fundamentals of Machine Learning for Predictive Data Analytics'? A: The book focuses on providing a comprehensive introduction to key machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
- Q: Who is the target audience for this textbook? A: The textbook is suitable for undergraduates in computer science, engineering, mathematics, or statistics, graduate students in related fields, and professionals seeking a reference for predictive data analytics.
- Q: What topics are covered in the book? A: It covers four main approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning, along with evaluation techniques and case studies.
- Q: How does the book help with understanding complex concepts? A: The book includes nontechnical explanations, mathematical models, algorithms, and detailed worked examples to aid comprehension of complex machine learning concepts.
- Q: Is there any practical application discussed in the book? A: Yes, the book features case studies that illustrate the application of machine learning models in real-world business contexts, detailing the project development phases.
- Q: What is the binding type of this textbook? A: The textbook is available in hardcover binding, which is durable and suitable for frequent use.
- Q: When was 'Fundamentals of Machine Learning for Predictive Data Analytics' published? A: The book was published on July 24, 2015.
- Q: How many pages does the book contain? A: The book contains a total of 624 pages.
- Q: What is the condition of the book upon purchase? A: The book is available in new condition.
- Q: Who is the author of the book? A: The book is authored by John D. Kelleher.