| Description |
1 online resource (156 pages) |
| Physical Medium |
polychrome |
| Description |
text file |
| Contents |
Cover; Title Page; Copyright and Credits; About Packt; Contributor; Table of Contents; Preface; Chapter 1: First Step Towards Supervised Learning; Technical requirements; An example of supervised learning in action; Logistic regression; Setting up the environment; Supervised learning; Hill climbing and loss functions; Loss functions; Measuring the slope of a curve; Measuring the slope of an Nd-curve; Measuring the slope of multiple functions; Hill climbing and descent; Model evaluation and data splitting; Out-of-sample versus in-sample evaluation; Splitting made easy; Summary |
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Chapter 2: Implementing Parametric ModelsTechnical requirements; Parametric models; Finite-dimensional models; The characteristics of parametric learning algorithms; Parametric model example; Implementing linear regression from scratch; The BaseSimpleEstimator interface; Logistic regression models; The concept; The math; The logistic (sigmoid) transformation; The algorithm; Creating predictions; Implementing logistic regression from scratch; Example of logistic regression; The pros and cons of parametric models; Summary; Chapter 3: Working with Non-Parametric Models; Technical requirements |
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The bias/variance trade-offError terms; Error due to bias; Error due to variance; Learning curves; Strategies for handling high bias; Strategies for handling high variance; Introduction to non-parametric models and decision trees; Non-parametric learning; Characteristics of non-parametric learning algorithms; Is a model parametric or not?; An intuitive example -- decision tree; Decision trees -- an introduction; How do decision trees make decisions?; Decision trees; Splitting a tree by hand; If we split on x1; If we split on x2; Implementing a decision tree from scratch; Classification tree |
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Regression treeVarious clustering methods; What is clustering?; Distance metrics; KNN -- introduction; KNN -- considerations; A classic KNN algorithm; Implementing KNNs from scratch; KNN clustering; Non-parametric models -- pros/cons; Pros of non-parametric models; Cons of non-parametric models; Which model to use?; Summary; Chapter 4: Advanced Topics in Supervised Machine Learning; Technical requirements; Recommended systems and an introduction to collaborative filtering; Item-to-item collaborative filtering; Matrix factorization; Matrix factorization in Python; Limitations of ALS |
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Content-based filteringLimitations of content-based systems; Neural networks and deep learning; Tips and tricks for training a neural network; Neural networks; Using transfer learning; Summary; Other Books You May Enjoy; Index |
| Summary |
A supervised learning task infers a function from flagged training data and maps an input to an output based on sample input-output pairs. In this book, you will learn various machine learning techniques (such as linear and logistic regression) and gain the practical knowledge you need to quickly and powerfully apply algorithms to new problems. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
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Python (Computer program language)
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Python (Computer program language) |
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Algorithms & data structures. |
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Data capture & analysis. |
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Programming & scripting languages: general. |
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Computers -- Data Processing. |
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Computers -- Programming -- Algorithms. |
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Computers -- Programming Languages -- Python. |
| Genre/Form |
Electronic books.
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| Other Form: |
Print version: Smith, Taylor. Supervised Machine Learning with Python : Develop Rich Python Coding Practices While Exploring Supervised Machine Learning. Birmingham : Packt Publishing, Limited, ©2019 9781838825669 |
| ISBN |
1838823069 |
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9781838823061 (electronic book) |
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