This is a self-paced lab that takes place in the Google Cloud console. Cloud Debugger lets developers debug running code with live request data. In this lab you will set breakpoints and log points on the fly to examine what caused an application's performance issues.

This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

This is a self-paced lab that takes place in the Google Cloud console. The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. In this lab you’ll send an image to the Cloud Vision API and have it identify objects, faces, and landmarks.

This is a self-paced lab that takes place in the Google Cloud console. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. This lab uses transfer learning to train your machine. In transfer learning, when you build a new model to classify your original dataset, you reuse the feature extraction part and re-train the classification part with your dataset. This method uses less computational resources and training time. Deep learning from scratch can take days, but transfer learning can be done in short order.

This course will help you recognize how the "digital thread" is the backbone of the digital manufacturing and design (DM&D) transformation, turning manufacturing processes from paper-based to digital-based. You will have a working understanding of the digital thread – the stream that starts at product concept and continues to accumulate information and data throughout the product’s life cycle – and identify opportunities to leverage it. Gain an understanding of how "the right information, in the right place, at the right time" should flow. This is one of the keys to unlocking the potential of a digital design process. Acknowledging this will enable you to be more involved in a product’s development cycle, and to help a company become more flexible. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the second course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

This is a self-paced lab that takes place in the Google Cloud console. This hands-on lab shows you how to publish and consume messages with a pull subscriber, using the Google Cloud command line. Watch the short video <A HREF="https://youtu.be/oKU2wbTXMTY">Simplify Event Driven Processing with Cloud Pub/Sub</A>.

By the end of this project, you will learn the basic concepts and fundamentals of Node-red. Node-RED is an opensource flow-based development tool for visual programming in javascript it allows the programmers to interconnect physical I/O, could based-systems, databases and different APIs, in many ways. originally, it was designed to work with the Internet of Things, i.e. devices that interact and control the real world, as it has evolved, it has become useful for a range of applications. In this project, we are going to cover key-core nodes in node-red. at the final task of this project we will create a weather alert application using node-red.

Predictive models attempt at forecasting future value based on historical data. In this hands-on project, we will analyze the transmission of Covid-19 virus across the globe and train a time-series model (fbprophet) to get the projection of corona virus-related cases in the United States.

In this 1-hour long project, you will learn how to clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling, and the impact that class imbalance of training data has on classification performance. You will learn how to use subword units to further mitigate the negative effects of class imbalance, and build an even better model.