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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.
What You Will Learn 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 Who This
Book Is For Data scientists and machine learning engineers keen on
exploring ensemble learning
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