Download Fundamentals of Machine Learning for Predictive Data by John D. Kelleher PDF

By John D. Kelleher

Machine studying is frequently used to construct predictive types through extracting styles from huge datasets. those types are utilized in predictive info analytics functions together with rate prediction, hazard overview, predicting purchaser habit, and record category. This introductory textbook deals a close and centred remedy of an important computing device studying ways utilized in predictive information analytics, overlaying either theoretical recommendations and sensible functions. Technical and mathematical fabric is augmented with explanatory labored examples, and case stories illustrate the applying of those types within the broader company context.

After discussing the trajectory from facts to perception to selection, the booklet describes 4 techniques to computing device studying: information-based studying, similarity-based studying, probability-based studying, and error-based studying. each one of those methods is brought by way of a nontechnical clarification of the underlying thought, through mathematical versions and algorithms illustrated by way of special labored examples. ultimately, the ebook considers ideas for comparing prediction versions and gives case reports that describe particular information analytics tasks via every one part of improvement, from formulating the enterprise challenge to implementation of the analytics resolution. The ebook, educated by way of the authors' a long time of training computing device studying, and dealing on predictive info analytics initiatives, is appropriate to be used by way of undergraduates in computing device technological know-how, engineering, arithmetic, or facts; by way of graduate scholars in disciplines with purposes for predictive information analytics; and as a reference for professionals.

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Extra resources for Fundamentals of Machine Learning for Predictive Data Analytics

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In machine learning terms, each row in the dataset is referred to as a training instance, and the overall dataset is referred to as a training dataset. 1 A credit scoring dataset. 15 default An example of a very simple prediction model for this domain would be if LOAN-SALARY RATIO > 3 then OUTCOME = default else OUTCOME = repay We can say that this model is consistent with the dataset as there are no instances in the dataset for which the model does not make a correct prediction. When new mortgage applications are made, we can use this model to predict whether the applicant will repay the mortgage or default on it and make lending decisions based on this prediction.

These case studies will demonstrate how everything described in the preceding chapters comes together in a successful predictive data analytics project. Finally, Chapter 11[511] provides some overarching perspectives on machine learning for predictive data analytics and summarizes some of the key differences between the different approaches covered in this book. What is predictive data analytics? What is supervised machine learning? What does this mean? Underneath the table we list two prediction models that are consistent with this dataset, Model 1 and Model 2.

The second disadvantage is that using a programming language means we have very little of the infrastructural support, such as data management, that is present in application-based solutions available to us. This puts an extra burden on developers to implement these supports themselves. The insights that these prediction models produce are used to help organizations make data-driven decisions. Machine learning, however, is made difficult because there is usually more than one model that is consistent with the training dataset—because of this, machine learning is often described as an ill-posed problem.

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