Welcome to Cloud Computing Basics (Cloud 101).
Over the next few weeks, we will discuss the basics of Cloud computing: what it is, what it supports, and how it is delivered. We will delve into storage services, Cloud economics, levels of managed infrastructure, and Azure services. We will also explore different deployment models of Cloud computing, as well as several hosting scenarios. Last but not least, we will compare some of the cloud platforms and discuss the future of cloud computing.
When do governments deserve our allegiance, and when should they be denied it?
This course explores the main answers that have been given to this question in the modern West. We start with a survey of the major political theories of the Enlightenment: Utilitarianism, Marxism, and the social contract tradition. In each case, we begin with a look at classical formulations, locating them in historical context, but then shift to the contemporary debates as they relate to politics today.
Next, we turn to the rejection of Enlightenment political thinking, again exploring both classical and contemporary formulations. The last part of the course deals with the nature of, and justifications for, democratic politics, and their relations to Enlightenment and Anti-Enlightenment political thinking.
In addition to exploring theoretical differences among the various authors discussed, considerable attention is devoted to the practical implications of their competing arguments. To this end, we discuss a variety of concrete problems, including debates about economic inequality, affirmative action and the distribution of health care, the limits of state power in the regulation of speech and religion, and difficulties raised by the emerging threat of global environmental decay.
In this course, you will:
a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image.
b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one.
c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images.
d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces.
The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models.
This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Strategies and Tools to Mitigate Agricultural Risk
Learners will examine risk management strategies for crop production, discuss techniques and programs to mitigate financial risk, and describe the types of rental agreements for agricultural land use. Learners will also explore traditional and innovative risk management strategies used in agricultural food production.
This course is part of the College of ACES suite of online programs, including the graduate certificate, "Agribusiness and Sustainable Food Production Economics" that can be stacked toward an advanced degree in the College of ACES or the Gies College of Business. To learn more about online programs from the College of ACES and explore ways to apply your Coursera work toward a degree program at the University of Illinois, visit ACES Online at acesonline.illinois.edu.
Diseña imprimibles para cursos en línea usando Canva
Al final de este proyecto, usted creará una variedad de plantillas para imprimibles de cursos usando Canva que podrá compartir o vender en portales. Usted podrá incorporar un esquema de color , imágenes, y otros elementos de diseño estético que agregan valor al material de marketing de un negocio. Usted aprenderá a utilizar y modificar plantillas para sus creaciones.
En este proyecto usaremos la versión gratis de Canva. Crear imprimibles de alta calidad es ideal para ofrecer un producto digital gratis o para comercializar su negocio y crecer/ desarrollarlo.
Nota: Este curso es de mayor utilidad para estudiantes que residen en la región de América del Norte. Actualmente, estamos trabajando para proporcionar la misma experiencia en otras regiones.
This course builds on some of the concepts covered in the earlier Google Sheets course.
In this course, you will learn how to apply and customize themes In Google Sheets, and explore conditional formatting options.
You will learn about some of Google Sheets’ advanced formulas and functions. You will explore how to create formulas using functions, and you will also learn how to reference and validate your data in a Google Sheet.
Spreadsheets can hold millions of numbers, formulas, and text. Making sense of all of that data can be difficult without a summary or visualization. This course explores the data visualization options in Google Sheets, such as charts and pivot tables.
Google Forms are online surveys used to collect data and provide the opportunity for quick data analysis. You will explore how Forms and Sheets work together by connecting collected Form data to a spreadsheet, or by creating a Form from an existing spreadsheet.
Learn how to use LinkedIn effectively to build your professional presence, grow your network, and unlock career opportunities. Develop practical skills to optimize your profile, create content, and engage with the right audience.
This course provides a step-by-step guide to using LinkedIn for professional growth and business development. You will start by building a strong and credible profile, understanding how to showcase your skills, experience, and achievements effectively.
You will then learn how to expand your professional network by connecting with the right people and using LinkedIn’s tools for communication, marketing, and recruitment. The course also introduces LinkedIn’s business features, including advertising and sales tools used by professionals and organizations.
A key focus is on content creation. You will learn how to share insights through posts, blogs, and visual content to increase visibility and establish authority in your field.
By the end of the course, you will be able to confidently use LinkedIn to build your personal brand, grow meaningful connections, and explore new career and business opportunities.
В этом проекте вы научитесь увеличивать число подписчиков через твиты.
Примечание. Этот курс изначально создан для учащихся из Северной Америки. На данный момент мы адаптируем его и для других регионов.
This course discusses topics in derivative pricing. The first module is designed to understand the Black-Scholes model and utilize it to derive Greeks, which measures the sensitivity of option value to variables such as underlying asset price, volatility, and time to maturity. Greeks are important in risk management and hedging and often used to measure portfolio value change. Then we will analyze risk management of derivatives portfolios from two perspectives—Greeks approach and scenario analysis. The second module reveals how option’s theoretical price links to real market price—by implied volatility. We will discuss pricing by volatility surface as well as explanations of volatility smile and skew, which are common in real markets. The third module involves topics in credit derivatives and structured products and focuses on Credit Debit Obligation (CDO), which played an important part in the past financial crisis starting from 2007. We will cover CDO’s definition, simple and synthetic versions of CDO, and CDO portfolios. The final module is the application of option pricing methodologies and takes natural gas and electricity related options as an example to introduce valuation methods such as dynamic programming in real options.
Who is this course for?
This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing.
You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.
However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R.
Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage.
It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on.
But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future.
That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
이 과정에서는 Cloud Spanner에 대해 알아봅니다. Cloud Spanner를 다른 데이터베이스 제품과 비교하여 언제, 어떻게 Spanner를 사용하여 규모에 맞추어 관계형 데이터베이스 요구사항을 해결할 수 있는지 알아봅니다. Google Cloud의 다양한 도구를 사용하여 Spanner 데이터베이스를 만들고 관리하는 방법, Spanner의 분산 데이터베이스 모델을 염두에 두고 관계형 스키마를 최적화하는 방법, Spanner API를 사용하여 Spanner 데이터베이스와 상호작용하는 방법, Spanner를 애플리케이션과 통합하는 방법, Spanner 데이터베이스와 자체 데이터를 관리하는 데 다른 Google 도구를 사용하는 방법을 알아봅니다.
