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Hands-on ensemble learning with Python : build highly optimized ensemble machine learning models using scikit-learn and Keras /
Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world da...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 020 | |a 9781789617887 | ||
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| 020 | |z 9781789612851 | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Kyriadides, George, |e author. | |
| 245 | 1 | 0 | |a Hands-on ensemble learning with Python : |b build highly optimized ensemble machine learning models using scikit-learn and Keras / |c George Kyriadides, Konstantinos G. Margaritis. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2019. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed October 30, 2019). | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and Keras Book Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter's streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com ... | ||
| 505 | 0 | |a Chapter 1: A Machine Learning Refresher -- Chapter 2: Getting Started with Ensemble Learning -- Chapter 3: Voting -- Chapter 4: Stacking -- Chapter 5: Bagging -- Chapter 6: Boosting -- Chapter 7: Random Forests -- Chapter 8: Clustering -- Chapter 9: Classifying Fraudulent Transactions -- Chapter 10: Predicting Bitcoin Prices -- Chapter 11: Evaluating Sentiment on Twitter -- Chapter 12: Recommending Movies with Keras -- Chapter 13: Clustering World Happiness. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 700 | 1 | |a Margaritis, Konstantinos G., |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781789612851/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||