Ensemble Learning for AI Developers

Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. You will:

• Understand the techniques and methods utilized in ensemble learning
• Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias

• Enhance your machine learning architecture with ensemble learning

Autorentext
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.

Mayank Jain currently works as Manager Technology at the Publicis Sapient Innovation Lab Kepler as an AI/ML expert. He has more than 10 years of industry experience working on cutting-edge projects to make computers see and think using techniques such as deep learning, machine learning, and computer vision. He has written several international publications, holds patents in his name, and has been awarded multiple times for his contributions.

Inhalt
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 techniquesChapter 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 ensemble learning in Kaggle competitions Useful examples and case studies
Chapter 7 : The Path ForwardChapter goal In this section we wil...