Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python

(ebook) (audiobook) (audiobook)

Autorzy:: Dipayan Sarkar, Vijayalakshmi Natarajan

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Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python Dipayan Sarkar, Vijayalakshmi Natarajan - okladka książki

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python Dipayan Sarkar, Vijayalakshmi Natarajan - audiobook MP3

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python Dipayan Sarkar, Vijayalakshmi Natarajan - audiobook CD

Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 336
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Do przechowalni

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.

The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.

By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

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O autorach książki

Dipayan Sarkar holds a Masters in Economics and comes with 17+ years of experience. Dipayan has won international challenges in predictive modeling and takes a keen interest in the mathematics behind machine learning techniques. Before opting to become an independent consultant and a mentor in the data science and machine learning space with various organizations and educational institutions, he had served in the capacity of a senior data scientist with Fortune 500 companies in the US and Europe. He is currently associated with Great Lakes Institute of Management as a visiting faculty (Analytics) and BML Munjal University as an adjunct faculty (Analytics and Machine Learning). He has co-authored a book on "Ensemble Machine Learning with Python" with PACKT Publishing.

Vijayalakshmi Natarajan holds an ME in Computer Science, comes with 4 years of industry experience. She is a data science enthusiast and is a passionate trainer in the field of data science & data visualization. She takes keen interests in deep diving into Machine Learning techniques. Her specialization includes machine learning techniques in the field of image processing.

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Szczegóły książki

Tytuł oryginału:: Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
ISBN Ebooka:: 978-17-891-3250-2, 9781789132502
Data wydania ebooka :: 2019-01-31 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku Pdf:: 17.2MB
Rozmiar pliku ePub:: 17.5MB
Rozmiar pliku Mobi:: 37.9MB

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Kategorie:
Programowanie
Programowanie » Python - Programowanie
Big Data
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Spis treści książki

1. Get Closer to Your Data with Exploratory Data Analysis
2. Getting Started with Ensemble Machine Learning
3. Resampling Methods
4. Statistical & Machine Learning Algorithms
5. Bag the Models with Bagging
6. When in Doubt, use Random Forest
7. Boost up Model Performance with Boosting
8. Blend it with Stacking
9. Homogeneous Ensemble for Hand-Written Digits Recognition
10. Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
11. Heterogeneous Ensemble for Sentiment Analysis using NLP
12. Heterogeneous Ensemble for Multi-Label Classification for Text Categorization

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