© 2020

Ensemble Learning for AI Developers

Learn Bagging, Stacking, and Boosting Methods with Use Cases


Table of contents

  1. Front Matter
    Pages i-xvi
  2. Alok Kumar, Mayank Jain
    Pages 1-10
  3. Alok Kumar, Mayank Jain
    Pages 11-29
  4. Alok Kumar, Mayank Jain
    Pages 31-48
  5. Alok Kumar, Mayank Jain
    Pages 49-60
  6. Alok Kumar, Mayank Jain
    Pages 61-96
  7. Alok Kumar, Mayank Jain
    Pages 97-129
  8. Back Matter
    Pages 131-136

About this book


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


Ensemble Learning Machine Learning Regression Supervised Learning Artificial Intelligence Python Deep Learning Neural Networks NumPy SciPy

Authors and affiliations

  1. 1.GurugramIndia
  2. 2.GurugramIndia

About the authors

Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient. He has extensive
experience 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.

Bibliographic information

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