Have you ever wonder about a scenario, where you want to share data with your friend for a limited periode of time? Do you want to architect a design to host a website and maintain it at a very low cost? Are you confident enough of build an architecture in cloud, where your webiste wont face anydown time? Do you know how to recover your data from any form of Disaster? If so, then this is the right porject for you. I can provide you an apt solution for all these problems using Amazon Cloud. AWS Cloud is making everything that was imposiible at one time to reality. Don't let any disaster to shut your data down. In this 2 hours project, you will learn how to host a 'static website' and to enable 'Cross-Region Replication' as a part of 'Disaster Recovery'. I am introducing a relatively new feature in AWS Cloud, called "CLOUD SHELL" in this project, where you will get a practical experience of some common usages of generating 'Pre-Signed URL' and allowing access for any users in public Internet. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This course introduces you to the principles of secure programming. It begins by discussing the philosophy and principles of secure programming, and then presenting robust programming and the relationship between it and secure programming. We'll go through a detailed example of writing robust code and we'll see many common programming problems and show their connection to writing robust, secure programs in general. We’ll examine eight design principles that govern secure coding and how to apply them to your own work. We’ll discuss how poor design choices drive implementation in coding. We’ll differentiate between informal, formal, and ad hoc coding methods. Throughout, methods for improving the security and robustness of your programs will be emphasized and you will have an opportunity to practice these concepts through various lab activities. A knowledge of the C programming language is helpful, but not required to participate in the lab exercises.

This course is primarily aimed at cloud professionals who are interested in learning about Tencent Cloud's system operations. It equips learners with a foundational knowledge in deploying, monitoring, and operating Tencent Cloud's basic products and prepares them to take the Tencent Cloud SysOps Associate examination. After completing this course, learners will be able to operate and maintain Tencent Cloud's basic products and deploy business on Tencent Cloud.

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

In this 1.5 hours class, you will get up to speed with GraphQL, a popular query language that is used by many companies including Facebook, and right here at Coursera! GraphQL provides a standardized and flexible way to structure queries and exchange data among various database and server platforms. We will focus on the syntax and the fundamentals like data types, arguments, aliases, fragments, directives, and mutations. By the end of this class, you will have wired up a mock auction website with a GraphQL server and made it functional! Javascript and REST API are required as prerequisites.

This is a self-paced lab that takes place in the Google Cloud console. Discover how easy it is to spin up a private blockchain network and create user accounts within the network using BlockApps' STRATO platform on Google Cloud. In this lab, you will see just how easy it is to launch a blockchain network. This lab will show you how to spin up a simple private blockchain network consisting of one node. By the end of this lab, you will see how launching a private blockchain network is "the easy part" and feel prepared to launch & reset STRATO networks as you add more complexity to them in the future.

This course presents how properties of acting systems and programs can be verified automatically. The basic notion is a transition system: any system that can be described by states and steps. We present how in CTL (computation tree logic) properties like reachability can be described. Typically, a state space may be very large. One way to deal with this is symbolic model checking: a way in which sets of states are represented symbolically. A fruitful way to do so is by representing sets of states by BDDs (binary decision diagrams). Definitions and basic properties of BDDs are presented in this course, and also algorithms to compute them, as they are needed for doing CTL model checking.

In this hands-on project, we will train a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. This project could be practically used by any company with social media presence to automatically predict customer's sentiment (i.e.: whether their customers are happy or not). The process could be done automatically without having humans manually review thousands of tweets and customer reviews. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

The world runs computers. From small to large businesses, from the CEO down to level 1 support staff, everyone uses computers. This course is designed to give you a practical perspective on computer security. This course approaches computer security in a way that anyone can understand. Ever wonder how your bank website is secure when you connect to it? Wonder how other business owners secure their network? Wonder how large data breaches happen? This is practical computer security. It will help you answer the question – what should I focus on?

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.