Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases - Softcover
Synopsis
Chapter 1: An Introduction to Ensemble Learning
Chapter Goal: This chapter will give you a brief overview of ensemble learning
No of pages - 10
Sub -Topics
Need for ensemble techniques in machine learning
Historical overview of ensemble learning
A brief overview of various ensemble techniques
Chapter 2: Varying Training Data
Chapter Goal: In this chapter we will talk in detail about ensemble techniques where training
data is changed.
No of pages: 30
Sub - Topics:
Use of bagging or bootstrap aggregating for making ensemble model
Code samples
Popular libraries support for bagging and best practices
Introduction to random forests models
Hands-on code examples for using random forest models
Introduction to cross validation methods in machine learning
Intro to K-Fold cross validation ensembles with code samples
Other examples of varying data ensemble techniques
Chapter 3: Varying Combinations
Chapter Goal : In this chapter we will talk about in detail about techniques where models are
used in combination with one another to getting an ensemble learning boost.No of pages: 40
Sub - Topics:
Boosting : We will talk in detail about various boosting techniques with historical
examples
Introduction to adaboost , with code examples , Industry best practices and useful state
of the art libraries for adaboost
Introduction to gradient boosting , with hands on code examples with useful libraries
and industry best practices for gradient boosting
Introduction to XGboost with hands on code examples with useful libraries and industry
best practices for XGboost
Stacking : We will talk in detail about various stacking techniques are used in machine
learning world Stacking in practice: How stacking is used by Kagglers for improving for winning
entries.
Chapter 4: Varying Models
Chapter Goal: In this chapter we will talk about how ensemble learning models could
lead to better performance of your machine learning project
No of pages: 30
Sub - Topics:
Training multiple model ensembles with code examples
Hyperparameter tuning ensembles with code examples
Horizontal voting ensembles
Snapshot ensembles and its variants, Introduction to the cyclic learning rate.
Code examples
Use of ensembles in the deep learning world.
Chapter 5: Ensemble Learning Libraries and How to Use Them
Chapter Goal: In this chapter we will go into details about some very popular libraries used by
data science practitioners and Kagglers for ensemble learning
No of pages: 25
Sub - Topics: Ensembles in Scikit-Learn
Learning how to use ensembles in TensorFlow
Implementing and using ensembles in PyTorch
Using Boosting using Microsoft LightGBM
Boosting using XGBoost
Stacking using H2O library
Ensembles in RChapter 6: Tips and Best Practices
Chapter Goal: In this chapter we will learn what are the best practices around ensemble learning with real world examples
No of pages: 25
Sub - Topics:
How to build a state of the art Image classifier using ensembles
How to use ensembles in NLP with real-world examples
Use of ensembles for structured data analysis
Using ensembles for time series data
Useful tips and pitfalls
How to leverage ense
"synopsis" may belong to another edition of this title.
