Modern Artificial Intelligence And Data Science

This Book, through its various chapters presenting the Recent Advances in Modern Artificial Intelligence and Data Science as well as their Applications, aims to set up lasting and real applications necessary for both academics and professionals. Readers find here the fruit of many research ideas covering a wide range of application areas that can be explored for the advancement of their research or the development of their business. These ideas present new techniques and trends projected in various areas of daily life. Through its proposals of new ideas, this Book serves as a real guide both for experienced readers and for beginners in these specialized fields. It also covers several applications that explain how they can support some societal challenges such as education, health, agriculture, clean energy, business, environment, security and many more. This Book is therefore intended for Designers, Developers, Decision-Makers, Consultants, Engineers, and of course Master's/Doctoral Students, Researchers and Academics.

The era of artificial intelligence has arrived. You, who only felt far from artificial intelligence, and the growing dream trees, are now inseparable from artificial intelligence. What does AI have to do with me? Isn't it a distant future that has nothing to do with me, not a scientist, a technician, or a computer programmer? Well, Artificial intelligence is not a story of someone who has nothing to do with it, but the fact is, it is now everyone's story. AI is already deeply infiltrating everyone's life. The question is no longer whether we use technology or not; it's about working together in a better way. Surrounding technologies like Siri, Alexa, or Cortana are seamlessly integrated into our interactions. We walk into the room, turn on the lights, play songs, change the room temperature, keep track of shopping lists, book a ride at the airport, or remind ourselves to take the proper medication on time. It is now necessary to look at artificial intelligence from a broader and larger perspective. You should not just hang on to complex deep learning algorithms and think only through science and technology but through the eyes of emotions and humanities. These days, elementary school students learn English and coding at school. Tomorrow's elementary school students will learn AI. Of course, not everyone needs to be an AI expert. But if you don't understand AI, you will be left out of the trend of changing times. AI comes before English and coding. This is because artificial intelligence is the language and tool of the future. This book opens your door to the most critical understanding needed of AI and other relevant disruptive technologies. Artificial intelligence will significantly change societal structures and the operations of companies. The next generation of employees needs to be trained as a workforce before entering the job market, and the existing workforce is regularly recharged and skilled. There is plenty on this for reskilling too. This is the most definitive compendium of AI, The Internet of Things, Machine Learning, Deep Learning, Data Science, Big Data, Cloud Computing, Neural networks, Robotics, the future of work and the future of intelligent industries.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

"The proposed book covers the topic of data science in a very comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The book starts from the basic concepts of data science; it highlights the types of data, its use and its importance, followed by discussion on a wide range of applications of data science and widely used techniques in data science. Key features: provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science, presents predictive outcomes by applying data science techniques on real life applications, provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods, and gives the reader variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful"--

This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focus on readers interested in machine learning, cognitive and neuro-inspired computational systems – theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions to applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. Overall, this book provides valuable information on effective, cutting-edge techniques and approaches for students, researchers, practitioners, and academicians working in the field of AI, neural network, machine learning, and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains.

With the help of in-depth explanations, concepts and ideas regarding machine learning and artificial intelligence, beginners can develop basic AI models that are capable of making accurate predictions and data insights with the best performance. Thus, this book is most suitable for beginners who have an interest in methodologies of artificial intelligence and deep learning. Big data analytics and data sciences have brought a major breakthrough in the current world by introducing models that bring long-term benefits to businesses and industries. By understanding the algorithms and approaches explained in this book, beginners can develop state of the art machine learning models for solving real work problems. By reviewing possible challenges, performances and implementations of artificial intelligence, neural networks, big data analytics and deep learning, applications of AI can be developed to improve workflow and system efficiency as well. Moreover, industries and businesses can greatly promote their sales, profits and performance by considering the techniques and methods set out in this book. However, this book is not only for beginners. Researchers and experts can learn about the latest the findings on artificial intelligence and machine learning for developing powerful algorithms and models. Artificial intelligence and machine learning approaches will definitely help in bringing positive changes to our daily lives. For learning and adopting the major findings, rules and techniques of artificial intelligence and deep learning, reading "Machine Learning For Beginners: The Ultimate Guide to Understand Artificial Intelligence and Big Data Analytics. Learn the Building Block Algorithms and the Machine Learning's Application in the Modern Life" will surely prove to be of great benefit for students and researchers.

This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.

Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.