Ensemble Machine Learning Cookbook
Over 35 practical recipes to explore ensemble machine learning techniques using Python
Published on 23. September 2024
336 pages
978-1-78913-250-2 (ISBN)
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Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
17,91 MB
ISBN-13
978-1-78913-250-2 (9781789132502)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Other editions
Additional editions
Dipayan Sarkar | Vijayalakshmi Natarajan
Ensemble Machine Learning Cookbook
Over 35 practical recipes to explore ensemble machine learning techniques using Python
Book
01/2019
Packt Publishing
€56.40
Shipment within 3-4 weeks
Persons
Sarkar Dipayan :
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.Natarajan Vijayalakshmi :
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.
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.Natarajan Vijayalakshmi :
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.
Content
Table of Contents
Get Closer to Your Data with Exploratory Data Analysis
Getting Started with Ensemble Machine Learning
Resampling Methods
Statistical & Machine Learning Algorithms
Bag the Models with Bagging
When in Doubt, use Random Forest
Boost up Model Performance with Boosting
Blend it with Stacking
Homogeneous Ensemble for Hand-Written Digits Recognition
Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
Heterogeneous Ensemble for Sentiment Analysis using NLP
Heterogeneous Ensemble for Multi-Label Classification for Text Categorization
Get Closer to Your Data with Exploratory Data Analysis
Getting Started with Ensemble Machine Learning
Resampling Methods
Statistical & Machine Learning Algorithms
Bag the Models with Bagging
When in Doubt, use Random Forest
Boost up Model Performance with Boosting
Blend it with Stacking
Homogeneous Ensemble for Hand-Written Digits Recognition
Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
Heterogeneous Ensemble for Sentiment Analysis using NLP
Heterogeneous Ensemble for Multi-Label Classification for Text Categorization
