By the end of this project, you will learn the fundamentals of how to use OWASP Zed Attack Proxy (ZAP). This tool greatly aids security professionals and penetration testers to discover vulnerabilities within web applications. You will learn how to perform a basic web app vulnerability scan, analyze the results, and generate a report of those results. This course includes steps on how to configure the browser proxy to passively scan web requests and responses by simply exploring websites. This course will also include how to use dictionary lists to find files and folders on a web server, and how to spider crawl websites to find all the links and URLs. Finally, the end of the course gives a brief overview of how to intercept, view, modify, and forward web requests that occur between the browser and web application. 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.

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

In this guided project, you will create and setup an Azure DevOps project; add teams to your project; create, import, and manage work items; customize the project’s boards; and explore different views of your work items, and organize them into sprints. 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.

Introduction to Cloud Identity serves as the starting place for any new Cloud Identity, Identity/Access Management/Mobile Device Management admins as they begin their journey of managing and establishing security and access management best practices for their organization. This 15-30 hour accelerated, one-week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more. Through a series of introductory lessons, step-by-step hands-on exercises, Google knowledge resources, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as new Cloud Identity Administrators. Learning Objectives By the end of this course participants will be able to: • Establish a Cloud Identity domain for their organization or personal domain. • Add users in order to practice user lifecycle management. • Modify user permissions to gain an understanding of core Cloud Identity features. • Add mobile devices within the Google Mobile Management module. • Modify mobile management policy sets to gain familiarity with product options. • Navigate the Reports module, and practice running reports. • Explore and apply different security protocols to the domain. IMPORTANT - Before you enroll, you should be prepared to: • Sign up for a free 14 day trial of Cloud Identity. You will need to enter payment method information. We will show you step-by-step how to cancel your account if you wish to end your Cloud Identity instance at the end of training and avoid being charged. • Purchase a new domain or use one that you already own. The domain you use IS NOT included as part of the Cloud Identity trial.

This is a self-paced lab that takes place in the Google Cloud console. This lab demonstrates some of the security concerns of a default GKE cluster configuration and the corresponding hardening measures to prevent multiple paths of pod escape and cluster privilege escalation

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how you can use Identity-Aware Proxy (IAP) TCP forwarding to enable administrative access to VM instances that do not have external IP addresses or do not permit direct access over the internet.

This is a self-paced lab that takes place in the Google Cloud console. A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this lab you'll see how they work, and try processing an image to extract features from it! You also explore pooling, which compresses your image and further emphasizes the features.

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.

Components are popular in engineering and used for building user interfaces and games. Components are elements that you can use in designs when working with Figma. They help to make your projects have consistency. Adding the concept of components to a design tool makes the composition of complex designs more consistent and efficient. The great part of components in Figma is that Figma wants to make components easy to learn and use while still being powerful enough for advanced users. Learning about components will help you to build things faster and more consistently. By the end of this project you will be able to manipulate components in your projects easier.

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.