Ensemble Machine Learning Cookbook
This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference to implement a wide range of tasks and solve the common and uncommon problems in ensemble machine learning domain.
Offered by
Difficulty Level
Intermediate
Completion Time
11h12m
Language
English
About Book
Who Is This Book For?
This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.
Ensemble Machine Learning Cookbook
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h12m total length
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
Related Resources
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