Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Fundamentals of Machine Learning for Predictive Data Analytics Algorithms Worked Examples and Case Studies 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 ofte

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  • Title: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
  • Author: John D. Kelleher Brian Macnamee Aoife D'Arcy
  • ISBN: 9780262029445
  • Page: 212
  • Format: Hardcover
  • 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, riskA 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 information based learning, similarity based learning, probability based learning, and error based 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.

    One thought on “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies”

    1. I genuinely applaud the authors for this book. Their work is highly commendable.For a novice like me, even reading the appendices was helpful. These fundamentals are definitely key to understanding the many advanced topics throughout the book. The authors disclose other essential concepts like co-variance and correlation in the chapters as well. This mandates reading each section of the book carefully and thoroughly.The book showcases illustrious work on CRISP-DM methodology, detailing the nuanc [...]

    2. Here and there this volume is about as clear as mud (as when talking about Bayesian networks or k-d trees). But that's the exception rather than the rule and the writing is largely exceptionally lucid and helpful with lots of concrete details for how to get the best results including two fairly extensive case studies. (That being said, I suggest supplementing the volume with other sources when things just aren't very clear.) I have had prior exposure to machine learning but still learned a good [...]

    3. This book is a fabulous introduction to machine learning and analytics. I recommend it to all of my students as one of the two books which they should read by the end of the course I teach on data science -- the other being Introduction to Statistical Learning. The authors covers a lot of practical information which you need to succeed in the real world, such as using the CRISP-DM workflow to ensure high quality data results, handling outliers and missing data, performing EDA. In addition, they [...]

    4. This is an excellent overview of concepts presented in a clear, straightforward manner with very good examples. The tables, figures, charts, and graphs were also very helpful as was the use of pseudocode. I recommend this book for anyone wanting a broad overview of machine learning concepts.

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