Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases

by Alok Kumar

Chapter 1: An Introduction to Ensemble LearningChapter This chapter will give you a brief overview of ensemble learningNo of pages - 10Sub -Topics Need for ensemble techniques in machine learning Historical overview of ensemble learning A brief overview of various ensemble techniquesChapter 2: Varying Training DataChapter In this chapter we will talk in detail about ensemble techniques where trainingdata is changed.No of 30Sub -  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 CombinationsChapter Goal : In this chapter we will talk about in detail about techniques where models areused in combination with one another to getting an ensemble learning boost.No of 40Sub -  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 How stacking is used by Kagglers for improving for winning entries.
Chapter 4: Varying ModelsChapter In this chapter we will talk about how ensemble learning models couldlead to better performance of your machine learning projectNo of 30Sub -  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 ThemChapter In this chapter we will go into details about some very popular libraries used bydata science practitioners and Kagglers for ensemble learningNo of 25Sub -  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 R
Chapter 6: Tips and Best PracticesChapter In this chapter we will learn what are the best practices around ensemble learning with real world examplesNo of 25Sub -  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

154 pages, Paperback

Published June 22, 2020