“Essential Concepts and Techniques of AI & ML” is a comprehensive textbook designed to demystify the complexities of Artificial Intelligence and Machine Learning for learners at all levels. The book covers a broad spectrum of topics, starting with an overview of the history and evolution of AI and ML, and progressing to advanced techniques and applications. Readers will explore key concepts such as supervised and unsupervised learning, neural networks, data preprocessing, and model evaluation. Each chapter is carefully structured to provide a balance between theory and practice, with numerous examples, illustrations, and hands-on exercises. The book also delves into the ethical considerations surrounding AI and ML, ensuring that readers are aware of the broader implications of these technologies. Additionally, it introduces popular tools and frameworks, offering practical guidance on how to implement AI and ML models. Whether you are pursuing a career in AI and ML or simply want to understand the technologies driving today’s innovations, this textbook offers the essential knowledge and skills needed to navigate and contribute to this dynamic field.

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various explainability methods for designing robust and scalable explainable ML systems • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems • Design user-centric explainable ML systems using guidelines provided for industrial applications Book Description Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn • Explore various explanation methods and their evaluation criteria • Learn model explanation methods for structured and unstructured data • Apply data-centric XAI for practical problem-solving • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others • Discover industrial best practices for explainable ML systems • Use user-centric XAI to bring AI closer to non-technical end users • Address open challenges in XAI using the recommended guidelines Who this book is for This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

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Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning KEY FEATURES ● Extensive examples of Machine Learning and Deep Learning principles. ● Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings. ● Numerous use cases with the code snippets and examples are presented. DESCRIPTION 'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples. This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment. Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included. WHAT YOU WILL LEARN ● Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs. ● Get to explore Time Series, Computer Vision and NLP models with insightful examples. ● Dive deeper into Activation and Loss functions with various scenarios. ● Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care. ● Build a strong foundation around AI, ML and Deep Learning principles and key concepts. WHO THIS BOOK IS FOR This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts. TABLE OF CONTENTS 1. Introduction 2. Supervised Machine Learning 3. System Analysis with Machine Learning/Un-Supervised Learning 4. Feature Engineering 5. Classification, Clustering, Association Rules, and Regression 6. Time Series Analysis 7. Data Cleanup, Characteristics and Feature Selection 8. Ensemble Model Development 9. Design with Deep Learning 10. Design with Multi Layered Perceptron (MLP) 11. Long Short Term Memory Networks 12. Autoencoders 13. Applications of Machine Learning and Deep Learning 14. Emerging and Future Technologies.

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL. · Comprehensive analyses and probabilistic approaches for ML and DL. · Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. · Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs.

Dive into the "AI Mastery Trilogy," the ultimate collection for professionals seeking to conquer the world of artificial intelligence (AI). This 3-in-1 compendium is meticulously crafted to guide you from the foundational principles of AI to the intricate mathematical frameworks and practical coding applications that will catapult your expertise to new heights. Book 1: "AI Basics for Managers" by Andrew Hinton is your gateway to understanding and implementing AI in business. It equips managers with the knowledge to navigate the AI landscape, identify opportunities, and lead their organizations toward a future of innovation and growth. Book 2: "Essential Math for AI" demystifies the mathematical backbone of AI, offering a deep dive into the core concepts that fuel AI systems. From linear algebra to game theory, this book is a treasure trove for anyone eager to grasp the numerical and logical foundations that underpin AI's transformative power. Book 3: "AI and ML for Coders" is the hands-on manual for coders ready to harness AI and machine learning in their projects. It provides a comprehensive overview of AI and ML technologies, practical coding advice, and ethical considerations, ensuring you're well-equipped to create cutting-edge, responsible AI applications. The "AI Mastery Trilogy" is more than just a set of books; it's a comprehensive learning journey designed to empower business leaders, mathematicians, and coders alike. Whether you're looking to lead, understand, or build the future of AI, this collection is an indispensable resource for mastering the art and science of one of the most exciting fields in technology. Embrace the AI revolution and secure your copy of the "AI Mastery Trilogy" today!