Chapter 1: An Introduction to Ensemble LearningChapter Goal: 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 Goal: In this chapter we will talk in detail about ensemble techniques where trainingdata is changed.No of pages: 30Sub - 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 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 pages: 40Sub - 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 ModelsChapter Goal: In this chapter we will talk about how ensemble learning models couldlead to better performance of your machine learning projectNo of pages: 30Sub - 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 ThemChapter Goal: In this chapter we will go into details about some very popular libraries used bydata science practitioners and Kagglers for ensemble learningNo of pages: 25Sub - 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 R
Chapter 6: Tips and Best PracticesChapter Goal: In this chapter we will learn what are the best practices around ensemble learning with real world examplesNo of pages: 25Sub - 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
About the Author: Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient. He has extensiveexperience in leading strategic initiatives and driving cutting-edge, fast-paced innovations. He won several awards and he is passionate about democratizing AI knowledge. He manages multiple non- profit learning and creative groups in NCR.