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Supervised Machine Learning with Python : Develop Rich Python Coding Practices While Exploring Supervised Machine Learning.
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 an...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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