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Know how Smart TVs, Smart Cars, Smart Homes, and Smart Cities are changing the World! Key Features Learn to successfully create, launch and manage the Internet of Things services Know the process of specifying, implementing, and deploying IoT services Learn the fundamentals of IoT services, building blocks and the key factors Learn the fast track approach to IoT Learn a dual perspective on the Internet of Things and ubiquitous computing Know detailed coverage of the underlying architecture, framework and state of the art methodologies Description The Internet of Things (IoT) not only connect people but will connect ‘smart’ homes, appliances, cars, offices, factories, cities, basically the world. This book discusses how smart cities strive to deploy and interconnect infrastructures and services to guarantee that the authorities and citizens have access to reliable and global customized services. The book describes a wide range of topics present in the design, development, and running of smart cities, ranging from big data management, Internet of Things, and sustainable urban planning. The technical aspects of smart cities enabled primarily by the Internet of Things, the socio-economic motivations and impact of smart city development are covered in this book. What You Will Learn The purpose of this book is to help you to work with cities and learn to develop them into smart cities. You will learn to develop a plan and learn what an smart city is, how to plan the smart city infrastructure and from where do you start while developing the smart city. You will learn what kind of planning is involved and about permitting, rent, acquisition, construction planning, with whom should you work? You can learn all this and more from case studies and deployment planning described in the book. Who this book is for Students studying IoT in universities and who want to know the fundamentals of the IoT business. For business executives and IoT startups. Table of Contents 1. Introduction 2. RFID and WSN: The Beginning 3. Interoperability of IoT Devices and Sensor (Semantic) Web 4. Cloud’s Internet of Things (IoT) 5. IoT and Edge Computing 6. IoT - Big Data Convergence with IoT Data 7. Introduction to (Big Data) Internet of Things Analytics and Streams 8. Operability Among IoT Clouds and Semantics 9. Edge and Analytics 10. To Conclude 11. Abbreviation 12. Bibliography About the Author Rashmi Nanda has done her B.Tech in Electronics and Instrumentation from Purushottam Institute of Engineering and Technology (PIET), Rourkela, Odisha. She has more than five years of experience in freelance content writing LinkedIn Profile: https://www.linkedin.com/in/rashmi-nanda-7bb0b995/
Solve business problems with data-driven techniques and easy-to-follow Python examples Key Features Essential coverage on statistics and data science techniques. Exposure to Jupyter, PyCharm, and use of GitHub. Real use-cases, best practices, and smart techniques on the use of data science for data applications. Description This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. What you will learn Rapid understanding of Python concepts for data science applications. Understand and practice how to run data analysis with data science techniques and algorithms. Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. Become self-sufficient to perform data science tasks with the best tools and techniques. Who this book is for This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Table of Contents 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All Chapters’ Learning About the Author Prateek Gupta is a Data Enthusiast and loves data-driven technologies. Prateek has completed his B.Tech in Computer Science & Engineering and he is currently working as a Data Scientist in an IT company. Prateek has a total 9 years of experience in the software industry, and currently, he is working in the computer vision area. Prateek has implemented various end-to-end Data Science projects for fishing, winery, and ecommerce clients. His implemented object detection and recognition models and product recommendation engines have solved many business problems of various clients. His keen area of interest is in natural language processing and computer vision. In his leisure time, he writes posts about artificial intelligence in his blog. Blog links: http://dsbyprateekg.blogspot.com/ LinkedIn Profile: https://www.linkedin.com/in/prateek-gupta-64203354/
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem. Key Features Develop a Conceptual and Mathematical understanding of Statistics Get an overview of Statistical Applications in Python Learn how to perform Hypothesis testing in Statistics Understand why Statistics is important in Machine Learning Learn how to process data in Python Description This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. What you will learn Understand the basics of Statistics Get to know more about Descriptive Statistics Understand and learn advanced Statistics techniques Learn how to apply Statistical concepts in Python Understand important Python packages for Statistics and Machine Learning Who this book is for This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. Table of Contents 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning About the Authors Himanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management. LinkedIn Profile: https://www.linkedin.com/in/himanshu-singh-2264a350/ Blog links: https://medium.com/@himanshuit3036 Facebook Profile: https://www.facebook.com/silli23
Understand the Impact of AI in Industries and Assess Your Organizational AI Readiness Key Features Proven real use-cases of AI with its benefits illustrated. Exposure to successful implementation of AI in 8+ sectors. Exclusive coverage for the leadership team to design AI strategy with calculated risks and benefits. Description This book brings you cutting-edge coverage on AI and its ability to create a perfect world or a perfect storm across industries. Equipped with numerous real-world use-cases, the book imparts knowledge on innovations with AI and a process to determine your organizational AI readiness. You will gain from ethical considerations, execution strategy and a comprehensive assessment of AI in your sector. The sectors covered include Healthcare, Education, Media & Telecom, Travel & Transportation, Governance, Agriculture, Manufacturing, Retail, Business Functions (Finance, HR, Law, Marketing & Sales), Offices and Personal Life. Apart from this, you will get acquainted with AI policies in the USA, China, Canada, UK, Germany, Australia, India, Russia, OECD and the EU. This book will assist you in understanding your organization's AI maturity and how to gain competitive advantage in your respective industry by introducing AI in the business culture. By the end of this book, you will get strategic insights on managing risk and advancing the AI mandate in your business practices. What you will learn Productive & destructive future possibilities with AI. AI's innovations and applications in different sectors. Ethical challenges & strategic considerations with AI. AI policies in some of the major economies. AI governance & maturity assessment for organizations. Who this book is for This book is helpful for those looking to grasp the current state and future possibilities of AI. This includes business and administrative educators, students and professionals. It is particularly useful for leaders who would like to focus on specific industries, assess their current state with AI and get their organizations to be AI ready. Table of Contents 1. AI is Everywhere 2. AI in Healthcare 3. AI in Education 4. AI in Transportation & Space 5. AI in Media & Communication 6. AI in Government 7. AI by Countries (US, China, EU, Canada, UK and India) 8. AI in Businesses & Value Chain 9. AI at Work 10. AI at Home & in Personal Life 11. Getting AI right in organizations About the Authors Dr. Cindy Gordon is a Governor General awardee and the CEO and founder of SalesChoice, an AI SaaS company for B2B sales. Winner of numerous awards for AI Disruption, she is a former Venture capitalist, Accenture Partner, Xerox GM and Citibank VP. Dr. Gordon is also the Canada national spokesperson for STEM and women in tech for CATA and the Co-Founder and Chair for AI Directory. LinkedIn Bio: linkedin.com/in/cigordon. Malay A. Upadhyay (MBA, M.Sc, B.Engg) is a Duke of Edinburgh awardee and a customer journey executive, experienced across three continents and certified in Machine Learning. As the CXO at SalesChoice, he trained 150+ managers on the basics of AI and its successful adoption. Malay drives the subject of AI Management as a board member, advisor, author, and online instructor. Blog links: www.TheUpadhyays.com
Doors are never locked for smart software and smart devices that are trained by smart people Key Features A book for everyone interested to know more about WSN, AI, and IoT Discover various Open source tools & techniques for research and development in these felids An easy-to-understand guide that will help you get familiar with the upcoming developments in WSN, AI, and IoT Description Almost every industry is looking for solutions for the best performance in the work that they produce. Researchers and developers are developing promising solutions that address the industrial problems to increase the effectiveness and efficiency of either the product or the service. This paradigm has changed the way many solutions and services are designed. Wireless Sensor Networks (WSN) are the backbone implementation for the Internet of Things (IoT) to be realized. For the IoT to produce efficient results, Artificial Intelligence (AI) becomes the key assistance, however, it needs careful modeling. The content for the book is planned and prepared in such a way that you will be able to understand the concept and can interpret it for their use. The concepts, technologies, processes that are discussed in the book are contemporary and futuristic. Every chapter is well planned to be a subsequent chapter for the previous. In the Summary section of each chapter, there are a few review questions and a case for research. What will you learn Learn about the most popular AI & IoT research topics Discover a few WSN, IoT and AI Simulators Get to know more about the fusion of Blockchain and IoT technologies Know more about the AI and IoT predictions in the global scenario