Kalidas Shetty

Dipayan Sarkar

The second book of the Food Biotechnology series, Functional Foods and Biotechnology: Biotransformation and Analysis of Functional Foods and Ingredients highlights two important and interrelated themes: biotransformation innovations and novel bio-based analytical tools for understanding and advancing functional foods and food ingredients for health-focused food and nutritional security solutions. The first section of this book provides novel examples of innovative biotransformation strategies based on ecological, biochemical, and metabolic rationale to target the improvement of human health relevant benefits of functional foods and food ingredients. The second section of the book focuses on novel host response based analytical tools and screening strategies to investigate and validate the human health and food safety relevant benefits of functional foods and food ingredients. Food biotechnology experts from around the world have contributed to this book to advance knowledge on bio-based innovations to improve wider health-focused applications of functional food and food ingredients, especially targeting non-communicable chronic disease (NCD) and food safety relevant solution strategies. Key Features: Provides system science-based food biotechnology innovations to design and advance functional foods and food ingredients for solutions to emerging global food and nutritional insecurity coupled public health challenges. Discusses biotransformation innovations to improve human health relevant nutritional qualities of functional foods and food ingredients. Includes novel host response-based food analytical models to optimize and improve wider health-focused application of functional foods and food ingredients. The overarching theme of this second book is to advance the knowledge on metabolically-driven food system innovations that can be targeted to enhance human health and food safety relevant nutritional qualities and antimicrobial properties of functional food and food ingredients. The examples of biotransformation innovations and food analytical models provide critical insights on current advances in food biotechnology to target, design and improve functional food and food ingredients with specific human health benefits. Such improved understanding will help to design more ecologically and metabolically relevant functional food and food ingredients across diverse global communities. The thematic structure of this second book is built from the related initial book, which is also available in the Food Biotechnology Series Functional Foods and Biotechnology: Sources of Functional Food and Ingredients, edited by Kalidas Shetty and Dipayan Sarkar (ISBN: 9780367435226) For a complete list of books in this series, please visit our website at: https://www.crcpress.com/Food-Biotechnology-Series/book-series/CRCFOOBIOTECH

Biotransformation and Analysis of Functional Foods and Ingredients

Functional Foods and Biotechnology

Vijayalakshmi Natarajan

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook Description Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes. What you will learnUnderstand how to use machine learning algorithms for regression and classification problemsImplement ensemble techniques such as averaging, weighted averaging, and max-votingGet to grips with advanced ensemble methods, such as bootstrapping, bagging, and stackingUse Random Forest for tasks such as classification and regressionImplement an ensemble of homogeneous and heterogeneous machine learning algorithmsLearn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoostWho this book is for This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

Dipayan Sarkar

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