2848 Learning courses in Glasgow delivered Live Online

Developing a solid foundation in Greek grammar will help you create your own sentences correctly and will also make it easier to improve your communication skills in both spoken and written Greek. So this course has been designed to help you steadily advance with the Greek language. Here, on the Greek Online School Learning Management System (LMS) you will find all the grammar phenomena that you need to know for the A2 Level (basic knowledge) in Greek, the language that influenced all European languages.

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Preparing for the Professional Data Engineer Examination course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This seminar is intended for individuals who want to gain intermediate knowledge of Sales. Overview Upon successful completion of this seminar, guests will gain intermediate knowledge of Sales Leadership and learning resource availability. In this seminar, guests will obtain knowledge in Sales Leadership, leveraging New Horizons' Leadership and Professional Development Program. Sales Leadership Session Sales Leadership Topics

Duration 2 Days 12 CPD hours This course is intended for Built for experienced IT Professionalsworking with Citrix Virtual Appsand Desktops who need to plan for,implement, or manage a ProvisioningServices environment. Potential studentsinclude administrators, engineers, andarchitects. Overview #NAME? In this course, students will learn to install and configure a highly available Citrix Provisioning farm according to leading practices. In this course, students will learn about the architecture, communication, and processes that make up Citrix Provisioning to be successful with deploying and managing a farm. Manage and integrate vDisks and target devices with Citrix Virtual Apps and Desktops for easy rollback, upgrades, and performance of Virtual Delivery Agent machines. At the end of this course students will be able to install, configure and manage the CitrixProvisioning 7 solution. Advanced Provisioning Learning Objectives Introduction to Citrix Provisioning (PVS) Getting Started with Citrix Provisioning Citrix Provisioning Architecture Citrix Provisioning Infrastructure Lab VM Power Management Learning Objectives The Citrix Provisioning Server The Farm Database The Store Streaming the vDisk Lab VM Power Management Learning Objectives vDisk Introduction Master Target Device Preparation Streaming Introduction Boot Methods Target Devices Lab VM Power Management Learning Objectives Target Devices Introduction Reads and Writes Machine and User Data Integrating Citrix Provisioning with Citrix Virtual Apps and Desktops Lab VM Power Management Learning Objectives The Complete Architecture Overview The Citrix Virtual Desktops Setup Wizard Manage the Target Devices through Creating Device Collections Using Provisioned Services with Citrix Virtual Apps and Desktops Managing Citrix Provisioning from Citrix Cloud Advanced Architecture Lab VM Power Management Learning Objectives Farm Component Scalability Store Redundancy Farm Database Redundancy Supporting Citrix Provisioning Lab VM Power Management Learning Objectives vDisk Updates Delegate Administration Audit and Support Alternative vDisk Update Methods

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Cloud professionals who intend to take the Professional Cloud Architect certification exam. Overview Candidates will be able to identify skill gaps and further areas of study. Candidates will also be directed to appropriate target learning resources. Students in this course will prepare for the Professional Cloud Architect Certification Exam. They will rehearse useful skills including exam question reasoning and case comprehension, tips and review of topics from the Infrastructure curriculum. Understanding the Professional Cloud Architect Certification Position the Professional Cloud Architect certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Cloud Architect and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies MountKirk Games Dress4Win TerramEarth Designing and Implementing Review the layered model from Design and Process Provide exam tips focused on business and technical design Designing a solution infrastructure that meets business requirements Designing a solution infrastructure that meets technical requirements Design network, storage, and compute resources Creating a migration plan Envisioning future solution improvements Resources for learning more about designing and planning Configuring network topologies Configuring individual storage systems Configuring compute systems Resources for learning more about managing and provisioning Designing for security Designing for legal compliance Resources for learning more about security and compliance Optimizing and Operating Analyzing and defining technical processes Analyzing and defining business processes Resources for learning more about analyzing and optimizing processes Designing for security Designing for legal compliance Resources for learning more about security and compliance Advising development/operation teams to ensure successful deployment of the solution Resources for learning more about managing implementation Easy buttons Playbooks Developing a resilient culture Resources for learning more about ensuring reliability Next Steps Present Qwiklabs Challenge Quest for the Professional CA Identify Instructor Led Training courses and what they cover that will be helpful based on skills that might be on the exam Connect candidates to individual Qwiklabs, and to Coursera individual courses and specializations. Review/feedback of course Additional course details: Nexus Humans Preparing for the Professional Cloud Architect Examination training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Preparing for the Professional Cloud Architect Examination course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Incoming attendees are required to have current, hands-on experience in developing basic web applications. Student should have some experience with HTML and CSS and be well versed in JavaScript. Experience with coding for the server side would be helpful. Overview This skills-focused course is approximately 50% hands-on. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Learn server-side JavaScript coding through Node.js Explore the latest JavaScript features, and ECMAScript modules Walk through different stages of developing robust applications using Node.js Install and use Node.js for development Use the Express application framework Work with REST service development using the Restify framework Use data storage engines such as MySQL, SQLITE3, and MongoDB Node.js is a server-side JavaScript platform using an event-driven, non-blocking I/O model allowing users to build fast and scalable data-intensive applications running in real time.This fast-paced hands-on course provides the core skills required to develop web applications with Node.js. You will progress from a rudimentary knowledge of JavaScript and server-side development to being able to create, maintain and test your own Node.js applications. You will explore the importance of transitioning to functions that return Promise objects, and the difference between fs, fs/promises and fs-extra, as well as how to use the HTTP Server and Client objects, and data storage with both SQL and MongoDB databases. Overview of Node.js The capabilities of Node.js Why should you use Node.js? The Node.js event-driven architecture Embracing advances in the JavaScript language Developing microservices or maxiservices with Node.js Setting Up Node.js System requirements Installing Node.js using package managers Installing from the source on POSIX-like systems Installing multiple Node.js instances with nvm Requirements for installing native code modules Choosing Node.js versions to use and the version policy Choosing editors and debuggers for Node.js Running and testing commands Advancing Node.js with ECMAScript 2015, 2016, 2017, and beyond Using Babel to use experimental JavaScript features Exploring Node.js Modules Defining a Node.js module Finding and loading modules using require and import Using npm ? the Node.js package management system The Yarn package management system HTTP Servers and Clients Sending and receiving events with EventEmitter Understanding HTTP server applications HTTP Sniffer ? listening to the HTTP conversation Web application frameworks Getting started with Express Creating an Express application to compute Fibonacci numbers Making HTTPClient requests Calling a REST backend service from an Express application Your First Express Application Exploring Promises and async functions in Express router functions Architecting an Express application in the MVC paradigm Creating the Notes application Theming your Express application Scaling up ? running multiple Notes instances Implementing the Mobile-First Paradigm Understanding the problem ? the Notes app isn't mobile friendly Learning the mobile-first paradigm theory Using Twitter Bootstrap on the Notes application Flexbox and CSS Grids Mobile-first design for the Notes application Using third-party custom Bootstrap themes Data Storage and Retrieval Remembering that data storage requires asynchronous code Logging and capturing uncaught errors Storing notes in a filesystem Storing notes with the LevelDB datastore Storing notes in SQL with SQLite3 Storing notes the ORM way with Sequelize Storing notes in MongoDB Additional course details: Nexus Humans Introduction to Node.js (TT4153) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Introduction to Node.js (TT4153) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects

Duration 4 Days 24 CPD hours This course is intended for This course is intended for security and network administrators who will be responsible for the installation, deployment, tuning, and day-to-day maintenance of the F5 Advanced Web Application Firewall. In this 4 day course, students are provided with a functional understanding of how to deploy, tune, and operate F5 Advanced Web Application Firewall to protect their web applications from HTTP-based attacks. The course includes lecture, hands-on labs, and discussion about different F5 Advanced Web Application Firewall tools for detecting and mitigating threats from multiple attack vectors such web scraping, Layer 7 Denial of Service, brute force, bots, code injection, and zero day exploits. Module 1: Setting Up the BIG-IP System Introducing the BIG-IP System Initially Setting Up the BIG-IP System Archiving the BIG-IP System Configuration Leveraging F5 Support Resources and Tools Module 2: Traffic Processing with BIG-IP Identifying BIG-IP Traffic Processing Objects Overview of Network Packet Flow Understanding Profiles Overview of Local Traffic Policies Visualizing the HTTP Request Flow Module 3: Web Application Concepts Overview of Web Application Request Processing Web Application Firewall: Layer 7 Protection F5 Advanced WAF Layer 7 Security Checks Overview of Web Communication Elements Overview of the HTTP Request Structure Examining HTTP Responses How F5 Advanced WAF Parses File Types, URLs, and Parameters Using the Fiddler HTTP Proxy Module 4: Common Web Application Vulnerabilities A Taxonomy of Attacks: The Threat Landscape What Elements of Application Delivery are Targeted? Common Exploits Against Web Applications Module 5: Security Policy Deployment Defining Learning Comparing Positive and Negative Security Models The Deployment Workflow Policy Type: How Will the Policy Be Applied Policy Template: Determines the Level of Protection Policy Templates: Automatic or Manual Policy Building Assigning Policy to Virtual Server Deployment Workflow: Using Advanced Settings Selecting the Enforcement Mode The Importance of Application Language Configure Server Technologies Verify Attack Signature Staging Viewing Requests Security Checks Offered by Rapid Deployment Defining Attack Signatures Using Data Guard to Check Responses Module 6: Policy Tuning and Violations Post-Deployment Traffic Processing Defining Violations Defining False Positives How Violations are Categorized Violation Rating: A Threat Scale Defining Staging and Enforcement Defining Enforcement Mode Defining the Enforcement Readiness Period Reviewing the Definition of Learning Defining Learning Suggestions Choosing Automatic or Manual Learning Defining the Learn, Alarm and Block Settings Interpreting the Enforcement Readiness Summary Configuring the Blocking Response Page Module 7: Attack Signatures & Threat Campaigns Defining Attack Signatures Attack Signature Basics Creating User-Defined Attack Signatures Defining Simple and Advanced Edit Modes Defining Attack Signature Sets Defining Attack Signature Pools Understanding Attack Signatures and Staging Updating Attack Signatures Defining Threat Campaigns Deploying Threat Campaigns Module 8: Positive Security Policy Building Defining and Learning Security Policy Components Defining the Wildcard Defining the Entity Lifecycle Choosing the Learning Scheme How to Learn: Never (Wildcard Only) How to Learn: Always How to Learn: Selective Reviewing the Enforcement Readiness Period: Entities Viewing Learning Suggestions and Staging Status Violations Without Learning Suggestions Defining the Learning Score Defining Trusted and Untrusted IP Addresses How to Learn: Compact Module 9: Cookies and Other Headers F5 Advanced WAF Cookies: What to Enforce Defining Allowed and Enforced Cookies Configuring Security Processing on HTTP headers Module 10: Reporting and Logging Overview: Big Picture Data Reporting: Build Your Own View Reporting: Chart based on filters Brute Force and Web Scraping Statistics Viewing F5 Advanced WAF Resource Reports PCI Compliance: PCI-DSS 3.0 The Attack Expert System Viewing Traffic Learning Graphs Local Logging Facilities and Destinations How to Enable Local Logging of Security Events Viewing Logs in the Configuration Utility Exporting Requests Logging Profiles: Build What You Need Configuring Response Logging Module 11: Lab Project 1 Lab Project 1 Module 12: Advanced Parameter Handling Defining Parameter Types Defining Static Parameters Defining Dynamic Parameters Defining Dynamic Parameter Extraction Properties Defining Parameter Levels Other Parameter Considerations Module 13: Automatic Policy Building Overview of Automatic Policy Building Defining Templates Which Automate Learning Defining Policy Loosening Defining Policy Tightening Defining Learning Speed: Traffic Sampling Defining Track Site Changes Lesson 14: Web Application Vulnerability Scanner Integration Integrating Scanner Output Importing Vulnerabilities Resolving Vulnerabilities Using the Generic XML Scanner XSD file Lesson 15: Deploying Layered Policies Defining a Parent Policy Defining Inheritance Parent Policy Deployment Use Cases Lesson 16: Login Enforcement and Brute Force Mitigation Defining Login Pages for Flow Control Configuring Automatic Detection of Login Pages Defining Session Tracking Brute Force Protection Configuration Source-Based Brute Force Mitigations Defining Credentials Stuffing Mitigating Credentials Stuffing Lesson 17: Reconnaissance with Session Tracking Defining Session Tracking Configuring Actions Upon Violation Detection Lesson 18: Layer 7 DoS Mitigation Defining Denial of Service Attacks Defining the DoS Protection Profile Overview of TPS-based DoS Protection Creating a DoS Logging Profile Applying TPS Mitigations Defining Behavioral and Stress-Based Detection Lesson 19: Advanced Bot Protection Classifying Clients with the Bot Defense Profile Defining Bot Signatures Defining Proactive Bot Defense Defining Behavioral and Stress-Based Detection Defining Behavioral DoS Mitigation Lesson 20: Form Encryption using DataSafe Targeting Elements of Application Delivery Exploiting the Document Object Model Protecting Applications Using DataSafe The Order of Operations for URL Classification Lesson 21: Review and Final Labs Review and Final Labs