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Learn AI & Machine Learning from the first principles. KEY FEATURESÊÊ _ Explore how different industries are using AI and ML for diverse use-cases. _ Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. _ Cutting-edge coverage on use of ML for business products and services. _ Explore how different companies are monetizing AI and ML technologies. _ Learn how you can start your own journey in the AI field from scratch. DESCRIPTION AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. WHAT YOU WILL LEARN Ê _ Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words. _ How you can leverage and integrate AI technologies in your business to differentiate your product in the market. _ The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. _ How AI can help companies in launching new products, improving existing ones and automating mundane processes. _ Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. WHO THIS BOOK IS FORÊÊ This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. TABLE OF CONTENTSÊ 1. Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid
Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower than anticipated, or so headlines may lead you to believe. With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization. Authors Rob Thomas and Paul Zikopoulos from IBM introduce C-suite executives and business professionals to the AI Ladder—a unified, prescriptive approach to help them understand and accelerate the AI journey. Complete with real-world examples and real-life experiences, this book explores AI drivers, value, and opportunity, as well as the adoption challenges organizations face. Understand why you can’t have AI without an information architecture (IA) Appreciate how AI is as much a cultural change as it is a technological one Collect data and make it simple and accessible, regardless of where it lives Organize data to create a business-ready analytics foundation Analyze data, and build and scale AI with trust and transparency Infuse AI throughout your entire business and create intelligent workflows
Artificial intelligence and machine learning are considered as hot technologies of this century. As these technologies move from research labs to enterprise data centers, the need for skilled professionals is continuously on the rise. This book is intended for IT and business professionals looking to gain proficiency in these technologies but are turned off by the complex mathematical equations. This book is also useful for students in the area of artificial intelligence and machine learning to gain a conceptual understanding of the algorithms and get an industry perspective. This book is an ideal place to start your journey as • Core concepts of machine learning algorithms are explained in plain English using illustrations, data tables and examples • Intuitive meaning of the mathematics behind popular machine learning algorithms explained • Covers classical machine learning, neural networks and deep learning algorithms At a time when the IT industry is focusing on reskilling its vast human resources, Machine intelligence is a very timely publication. It has a simple approach that builds up from basics, which would help software engineers and students looking to learn about the field as well as those who might have started off without the benefit of a structured introduction or sound basics. Highly recommended. - Siddhartha S, Founder and CEO of Intain - Financial technology startup Suresh has written a very accessible book for practitioners. The book has depth yet avoids excessive mathematics. The coverage of the subject is very good and has most of the concepts required for understanding machine learning if someone is looking for depth. For senior management, it will provide a good overview. It is well written. I highly recommend it. - Whee Teck ONG, CEO of Trusted Source and VP of Singapore Computer Society
The concept of Smart Cities is accurately regarded as a potentially transformative power all over the world. Bustling metropolises infused with the right combination of the Internet of Things, artificial intelligence, big data, and blockchain promise to improve both our daily lives and larger structural operations at a city government level. The practical realities pose challenges that a significant sector of the tech industry now revolves around solving. Cut through the hype with Demystifying Smart Cities. In this book, the real-world implementations of successful Smart City technology in places like New York, Amsterdam, Copenhagen, and more are analyzed, and insights are gained from recorded attempts in similar urban centers that have not reached their full Smart City potential. From the logistical complications of securing thousands of devices to collect millions of pieces of data daily, to the complicated governmental processes that are required to install Smart City tech, Demystifying Smart Cities covers every aspect of this revolutionary modern technology. This book is the essential guide for anybody who touches a step of the Smart City process—from salespeople representing product vendors to city government officials to data scientists—and provides a more well-rounded understanding of the full positive and negative impacts of Smart City technology deployment. Demystifying Smart Cities evaluates how our cities can behave in a more intelligent way, and how producing novel solutions can pose equally novel challenges. The future of the metropolis is here, and the expert knowledge in the book is your greatest asset. What You'll LearnPractical issues and challenges of managing thousands and millions of IoT devices in a city The different types of city data and how to manage and secure it The possibilities of utilizing AI into a city (and how it differs from working with the private sector) Examples of how to make cities smarter with technology Who This Book Is For Primarily for those already familiar with the hype of smart city technologies but not the details of its implementation, along with technologists interested in learning how city government works when integrating technology. Also, people working for smart city vendors, especially sales people and product managers who need to understand their target market.
Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.
A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.