The capstone will develop a professional-quality web portfolio. Students will demonstrate the ability to design and implement a responsive site for a minimum of three platforms. Adherence to validation and accessibility standards will be required. The evolving student implementations will be reviewed each week by capstone peers and teaching assistants to make sure that the student keeps up with the agenda of the course. Upon completion of this course students will feel comfortable creating and/or updating existing front-end sites, utilizing existing frameworks, and testing sites for accessibility compliance. This course is only open to students who have completed the first four courses in the Web Design for Everybody specialization: Introduction to HTML5, Introduction to CSS3, Interactivity with JavaScript, and Advanced Styling with Responsive Design.

This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered. 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.

In App Design and Development for iOS, the third course of the iOS App Development with Swift specialization, you will be developing foundational programming skills to support graphical element presentation and data manipulation from basic functions through to advanced processing. You will continue to build your skill set to use and apply core graphics, touch handling and gestures, animations and transitions, alerts and actions as well as advanced algorithms, threading and more. By the end of this course you will be able to develop a more advanced, fully functioning app. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.

In this project you will use MySQL Workbench to write SQL queries that aggregate (group) data. Incorporating aggregate functions like COUNT, SUM, and AVG, your SQL queries will group and summarize data. Data that is aggregated and presented in a logical format makes it a more valuable decision-making tool for users. 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 is a self-paced lab that takes place in the Google Cloud console. Google Compute Engine lets you create and run virtual machines on Google infrastructure. In this lab you create a Windows Server instance in the Google Compute Engine and access it with RDP. Watch a short preview, Launch a Windows Server Instance, GCP Essentials.

System Validation is the field that studies the fundamentals of system communication and information processing. It allows automated analysis based on behavioural models of a system to see if a system works correctly. We want to guarantee that the systems does exactly what it is supposed to do. The techniques put forward in system validation allow to prove the absence of errors. It allows to design embedded system behaviour that is structurally sound and as a side effect enforces you to make the behaviour simple and insightful. This means that the systems are not only behaving correctly, but are also much easier to maintain and adapt. ’Modeling Software Protocols, and other behaviour' demonstrates the power of formal methods in software modelling, communication protocols, and other examples. Reading material. J.F. Groote and M.R. Mousavi. Modeling and analysis of communicating systems. The MIT Press, 2014.

In this course you will learn to interpret and draw a relational model through hands-on exercises using a diagramming tool called “Dia”. You will complete the final step in the database design process as you convert the logical design documented in an Entity Relationship Diagram into a Relational Model. During the conversion, you will investigate relational modeling rules and practice modeling techniques as you learn to resolve one-to-many and many-to-many relationships using foreign keys and bridge tables. Your final Relational Model will become the blueprint for creating a database and its tables. Since data is at the center of any information system, knowledge and understanding of database design will serve you well as a database user or a database designer. 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.

In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well. In order to be successful in this project, you should be familiar with python programming, and basics of neural networks. 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.

In this 1-hour 30-minutes long project-based course, you will learn how to (identify different data types in java, conditional statements , scanner input & iteration loops). Through the virtual book store project you can choose the book you want from the preferred genre and the best price range that suits you. By first an interactive menu appear to the user to enter his preferred genre of the book( crime, drama), then another menu appear when he enters his genre preference, to choose his price range ( cheap , expensive ). Then another menu appear showing him the option that he can buy per his choice of genre and price range. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.