In this course, you will how to leverage Azure's Machine Learning capabilities to greatly increase the chance of success for your data science project. First, you will engage in team workflow and how Microsoft's Team Data Science Process (TDSP) enables best practices across disciplines. Then, you will discover the workflow of the Azure Machine Learning Service and how it can be leveraged on your project. You will also review how to create a pipeline for your data preparation, model training, and model registration. At the end of this course, you will explore the infrastructure approaches that can be leveraged for machine learning and how those approaches are supported on Azure.
Artificial Intelligence (AI) is intelligence exhibited by machines. In Computer Science, AI research deals with how to create computers that are capable of intelligent behavior. AI has been defined in numerous ways, but in general, it can be described as a way of making a computer system "smart" – able to understand complex tasks and carry out complex commands. The principal benefit of AI is that it can help humans make better decisions by providing insights and recommendations informed by data. AI has several applications and is being employed in a growing number of industries, including healthcare, finance, manufacturing, and transportation. Some of the most remarkable applications of AI are in the field of robotics, where AI is used to create machines that can carry out complex tasks.
Ishaan and Elizabeth, both graduate students in business, are attending a marketing strategy lecture at a business school in the Northeast. While learning about the principles of market segmentation, Ishaan texts "outdated" followed by three thinking--face emojis to Elizabeth. He wonders how demographic-, geographic-, or psychographic-based segmentation--the topic of the lecture--can help his family's franchise restaurant deal with the hundreds of sometimes-not-so-positive online reviews and social media posts. Meanwhile, Elizabeth hopes that the fast-food restaurant where she ordered her lunch understands that she now belongs to the segment of'extremely displeased' customers. Earlier, she used the restaurant's new app to order a burrito without cheese and sour cream, only to discover that the meal included both offending ingredients. Her lunch went straight into the trash can and she angrily tweeted her disappointment to the restaurant. This simple vignette illustrates an important point. Organizations of every size are challenged with capitalizing on enormous amounts of unstructured organizational data--for instance, from social media posts--particularly for applications such as market segmentation. The purpose of this article is to give the reader an idea of the challenges and opportunities faced by businesses using market segmentation, including the impacts of big data. Our research will demonstrate what market segmentation might look like in the near future, as we also offer a promising approach to implementing market segmentation using unstructured data.
On this training you will learn the basic concepts for Microsoft and Azure Cloud to understand the concepts and features to design and plan your Power Automate Implementation for your Business. Microsoft Power Platform is a line of business intelligence, app development, and app connectivity software applications. Microsoft developed the Power Fx low-code programming language for expressing logic across the Power Platform. It also provides integrations with GitHub and Teams. Power Automate is a versatile automation platform that integrates seamlessly with hundreds of apps and services. Power Automate can be used to get notifications, synchronize files, approve requests, collect data, and much more.
Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you'll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.
The information technology field offers incredible opportunities for skilled professionals, and a computer science master's degree puts graduates in a position to capitalize. The U.S. Bureau of Labor Statistics (BLS) projects the addition of more than 660,000 new computer occupations between 2020 and 2030. An advanced computer science degree can lead to some of the most in-demand positions among them. Master's graduates are equipped to work in cybersecurity, big data, cloud computing, and software and application development -- some of the fastest-growing and most integral IT fields. Here, we rank the best computer science master's programs in the country. We also examine the computer science discipline and degree levels more closely.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.
A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. When deploying these systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in uncertain, complex, and unpredictable real-world contexts. We are also identifying a number of urgent systems challenges for trustworthy embodied systems, including robust and human-centric AI, cognitive architectures, uncertainty quantification, trustworthy self-integration, and continual analysis and assurance.