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 learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce biasEnhance your machine learning architecture with ensemble learning Who This Book Is For Data scientists and machine learning engineers keen on exploring ensemble learning

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

AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today. Explore the options for implementing ML and AI on mobile devices Create ML models for iOS and Android Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS Choose the best techniques and tools for your use case, such as cloud-based versus on-device inference and high-level versus low-level APIs Learn privacy and ethics best practices for ML on devices

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

"Artificial Intelligence and Machine Learning: A Comprehensive Guidebook" offers a masterful exploration into the dynamic fields of AI and ML, providing readers with a thorough understanding of foundational concepts and cutting-edge advancements. This guidebook seamlessly navigates through essential topics including supervised and unsupervised learning, neural networks, and reinforcement learning. Each chapter is crafted to build on the previous, ensuring a cohesive learning experience for both beginners and advancing practitioners, as it demystifies complex subjects with clarity and precision. Beyond technical exposition, the book delves into the pivotal ethical considerations and future trends, critically examining AI's societal impact and the responsible development practices required to harness its potential. It addresses pressing challenges such as bias, accountability, and the evolving regulatory landscape while highlighting the transformative applications in sectors like healthcare, finance, and autonomous systems. As a definitive resource in the AI landscape, this guidebook equips readers with the knowledge to navigate and innovate in the rapidly evolving world of artificial intelligence and machine learning.

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From conception to launch, 'App Mastery: The Complete Guide to Successful App Development' takes aspiring developers on a comprehensive journey through the intricate world of app creation. Dive into the art of coding, explore advanced features, master marketing strategies, and discover how to scale and monetize your app effectively. With practical insights, expert tips, and real-world examples, this guide empowers developers to turn their app dreams into reality and navigate the competitive landscape of the digital marketplace with confidence.

"Practical AI Ethics: Integrating Ethical Principles into Machine Learning Projects" is an essential resource for AI professionals, policymakers, and academics dedicated to embedding ethical practices within the rapidly evolving field of machine learning. This comprehensive guide tackles some of the most pressing ethical challenges, including transparency, bias, privacy, fairness, and compliance, offering clear and actionable strategies for addressing these issues in AI systems. Written in a practical and solution-oriented style, the book simplifies complex ethical concepts, providing readers with advanced tools, practical frameworks, and insightful case studies to guide the ethical integration of AI in real-world projects. From minimizing the environmental impact of AI to safeguarding human rights and navigating regulatory landscapes, this book equips readers to take on the ethical challenges of AI with confidence. By engaging with *"Practical AI Ethics: Integrating Ethical Principles into Machine Learning Projects,"* readers will gain the knowledge and skills to lead the charge in promoting fairness, accountability, and transparency in AI. It is a must-read for anyone committed to shaping a responsible, ethical future for AI innovation.