683 Practitioner courses delivered Live Online

Duration 0.5 Days 3 CPD hours This course is intended for This course is primarily designed for business leaders, consultants, product and project managers, and other decision-makers who are interested in growing the business by leveraging the power of AI. Other individuals who wish to explore basic AI concepts are also candidates for this course. This course is also designed to assist students in preparing for the CertNexus AIBIZ™ (Exam AIZ-210) credential. Overview In this course, you will identify ways in which AI can bring significant value to the business. You will: Describe AI fundamentals. Identify the functions of AI in business. Implement business requirements for AI. Artificial intelligence (AI) is not just another technology or process for the business to consider?it is a truly disruptive force, one that delivers an entirely new level of results across business sectors. Even organizations that resist adopting AI will feel its impact. If the organization wants to thrive and survive in this transforming business landscape, it will need to harness the power of AI. This course is designed to help business professionals conquer and move beyond the basics of AI to apply AI concepts for the benefit of the business. It will give you the essential knowledge of AI you'll need to steer the business forward. Lesson 1: AI Fundamentals Topic A: A Brief History of AI Topic B: AI Concepts Lesson 2: Functions of AI in Business Topic A: Improve User Experiences Topic B: Segment Audiences Topic C: Secure Assets Topic D: Optimize Processes Lesson 3: Implementing Business Requirements for AI Topic A: Identify Design Requirements Topic B: Identify Data Requirements Topic C: Identify Risks in Implementing AI Topic D: Develop an AI Strategy

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators and network administrators Customers, cloud architects, systems engineers, data center administrators Network administrators with experience in managed services or managing a Telco Cloud environment Overview By the end of the course, you should be able to meet the following objectives: Deploy VMware Telco Cloud Service Assurance Manage VMware Telco Cloud Service Assurance to satisfy Telco cloud provider needs Discuss configurable options for VMware Telco Cloud Service Assurance Identify and configure different data sources which are used with VMware Telco Cloud Service Assurance Configure different collectors in VMware Telco Cloud Service Assurance Identify the Root Cause Analysis options with VMware Telco Cloud Service Assurance Discuss data collection in VMware Telco Cloud Service Assurance Explain root cause analysis in VMware Telco Cloud Service Assurance Navigate through the logs for troubleshooting This three-day, hands-on training course provides the knowledge, skills, and tools to achieve competency in installing, configuring, and managing the VMware Telco Cloud Service Assurance environment. In this course, you are introduced to the installation methods of VMware Telco Cloud Service Assurance? across various supported platforms and troubleshooting tools that help you install, manage, and troubleshoot your VMware Telco Cloud Service Assurance environment. In addition, you are presented with various types of configuration options, which you will identify, analyze, and navigate through as you explore the UI and configurable options of the product. Course Introduction Introduction and course logistics Course objectives Introduction to VMware Telco Cloud Service Assurance Describe the features of VMware Telco Cloud Service Assurance List the capabilities of VMware Telco Cloud Service Assurance Discuss the use cases of VMware Telco Cloud Service Assurance Describe the role played by VMware Telco Cloud Service Assurance components in delivering service assurance Deploying VMware Telco Cloud Service Assurance Explain different deployment options of VMware Telco Cloud Service Assurance Identify different deployment methods of VMware Telco Cloud Service Assurance Discuss different phases in deploying VMware Telco Cloud Service Assurance Identify different footprints available for HA based and non-HA based installation of VMware Telco Cloud Service Assurance Describe the SMARTs components of VMware Telco Cloud Service Assurance Deploy VMware Telco Cloud Service Assurance User Access Control Describe the features Role-based Access Control (RBAC) Outline the role of Keycloak in implementing RBAC in VMware Telco Cloud Service Assurance Configure user federation in Keycloak Use the VMware Telco Cloud Service Assurance UI to manage RBAC Create policies in VMware Telco Cloud Service Assurance that align with job roles Services and User Interface Configurations Describe the architecture of logical switching Describe the core services on a TCSA cluster Discuss the Global Manager or Service Assurance Manager (SAM), IP Domain Manager, Server Manager (ESM) Discuss VMware Telco Cloud Service Assurance UI Overview Explain Working with Notifications Elaborate Configuring Summary's Describe Accessing Notification Details Explain Viewing and configuring Topologies List Customizing Topologies Describe Topology Explorer Explain Collecting Troubleshooting Information Discuss Custom models Describe how compute resources are provided to VMware Telco Cloud Service Assurance Describe how storage is provided to VMware Telco Cloud Service Assurance Configure and manage VMware Telco Cloud Service Assurance Discuss configurable options for VMware Telco Cloud Service Assurance Day 1 and Day 2 Operations Review the architecture of logical routing and NSX Edge nodes Identify different data sources to be used with VMware Telco Cloud Service Assurance Configure different collectors with VMware Telco Cloud Service Assurance Describe Alarms and Thresholds Demonstrate how to configure alarms with VMware Telco Cloud Service Assurance Explain how to setup thresholds and timelines in VMware Telco Cloud Service Assurance Define Catalog management and sharing catalogs inside and between organizations. Identify the steps to import or upload data into catalogs. Explain the purpose of catalogs and How to Create a catalog organization. Describe the Purpose and Usage of Open Virtualization Format (OVA) and Custom vApp or VM Properties. Discuss vApp Templates Logs and Troubleshooting Review the architecture of the Distributed Firewall Discuss VMware Telco Cloud Service Assurance installations logs List Smarts installation logs Explain backup and restore options of VMware Telco Cloud Service Assurance Identify the approach for troubleshooting containerized services Discuss monitoring services

Duration 3 Days 18 CPD hours This course is intended for Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with a focus on Google Compute Engine. Overview Configure VPC networks and virtual machines Administer Identity and Access Management for resources Implement data storage services in GCP Manage and examine billing of GCP resources Monitor resources using Stackdriver services Connect your infrastructure to GCP Configure load balancers and autoscaling for VM instances Automate the deployment of GCP infrastructure services Leverage managed services in GCP This class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Introduction to Google Cloud Platform List the different ways of interacting with GCP Use the GCP Console and Cloud Shell Create Cloud Storage buckets Use the GCP Marketplace to deploy solutions Virtual Networks List the VPC objects in GCP Differentiate between the different types of VPC networks Implement VPC networks and firewall rules Design a maintenance server Virtual Machines Recall the CPU and memory options for virtual machines Describe the disk options for virtual machines Explain VM pricing and discounts Use Compute Engine to create and customize VM instances Cloud IAM Describe the Cloud IAM resource hierarchy Explain the different types of IAM roles Recall the different types of IAM members Implement access control for resources using Cloud IAM Storage and Database Services Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable Choose a data storage service based on your requirements Implement data storage services Resource Management Describe the cloud resource manager hierarchy Recognize how quotas protect GCP customers Use labels to organize resources Explain the behavior of budget alerts in GCP Examine billing data with BigQuery Resource Monitoring Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring Use Stackdriver Debugger to identify and fix errors Interconnecting Networks Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Determine which GCP interconnect or peering service to use in specific circumstances Create and configure VPN gateways Recall when to use Shared VPC and when to use VPC Network Peering Load Balancing and Autoscaling Recall the various load balancing services Determine which GCP load balancer to use in specific circumstances Describe autoscaling behavior Configure load balancers and autoscaling Infrastructure Automation Automate the deployment of GCP services using Deployment Manager or Terraform Outline the GCP Marketplace Managed Services Describe the managed services for data processing in GCP Additional course details: Nexus Humans Architecting with Google Compute Engine 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 Architecting with Google Compute Engine 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 5 Days 30 CPD hours This course is intended for This course is designed for professionals in job roles such as: Communication engineers Project managers Network engineers Software engineers System architects The Developing Applications for Cisco Webex and Webex Devices (DEVWBX) v1.1 course prepares you to use the programmability features of Webex©, Cisco© enterprise solution for video conferencing, online meetings, online training, webinars, web conferencing, cloud calling, and collaboration. Through a combination of lessons and hands-on labs, you will learn about Webex Application Programming Interface (API) Foundation, meetings, devices, teams, messaging, embedding Cisco Webex, administration, and compliance. You will learn how to leverage Webex APIs to extend the functionalities of teams, meetings, and devices, and explore how these APIs can help automate, administer, and enforce compliance. This course prepares you for the 300-920 Developing Applications for Cisco Webex and Webex Devices (DEVWBX) exam. Introducing Webex APIs Foundations Webex as an Extensible Platform Building Cisco Webex Teams Applications Introduction to Webex Messaging Developing with Webex Meetings XML API Describe the Capabilities of Cisco Webex Meetings APIs Automating and Extending Cisco Collaboration Devices with xAPI Overview, Capabilities and Transport Methods for Cisco Endpoint Device Programmability Embedding Cisco Webex Benefits of Embedding Cisco Webex into Other Applications Managing Administration and Compliance with Cisco Webex APIs Administer a Cisco Webex Organization

Duration 2 Days 12 CPD hours This course is intended for System administrators and consultants, application owners, and system architects Overview By the end of the course, you should be able to meet the following objectives: Describe VMware Carbon Black Cloud platform Describe data flows on VMware Carbon Black Cloud Create and edit a custom role in VMware Carbon Black Cloud Recognize the impact of a user role on a console user Describe the VMware Carbon Black Cloud sensor resource usage Explain sensor usage in VMware Carbon Black Cloud Identify configuration settings for endpoints in sensor policy settings Determine requirements for initial deployment of sensors Recognize the differences between attended and unattended sensor installation methods Identify the correct deployment strategy for a given scenario Recognize the deployment process for VMware Carbon Black Cloud Workload™ Identify eligible workloads in a VMware vSphere environment Describe VMware Carbon Black Cloud sensor deployment Manage VMware vSphere workloads Identify sensor status in RepCLI This two-day hands-on training course provides you with the knowledge, skills, and tools to achieve competency in planning and deploying VMware Carbon Black Cloud in your environment. This course explains the VMware Carbon Black Cloud components, managing users and roles in VMware Carbon Black Cloud, configuring policies to support sensor deployment and management, and presents methods for deploying sensors across endpoints and workloads. Course Introduction Introductions and course logistics Course objectives Introduction to VMware Carbon Black Cloud Describe the VMware Carbon Black Cloud platform Describe VMware Carbon Black Cloud operating systems requirements Identify interesting files according to VMware Carbon Black Cloud Identify events collected Describe data flows Managing VMware Carbon Black Cloud Roles and Users Describe the use of roles in VMware Carbon Black Cloud Describe RBAC capabilities Create and edit a custom role Manage new console users Recognize the impact of a user role on a console user Describe authentication mechanisms VMware Carbon Black Cloud Sensors Describe the VMware Carbon Black Cloud sensor resource usage List the supported operating systems for VMware Carbon Black Cloud sensors Explain sensor usage in VMware Carbon Black Cloud Preparing for Deployment Identify configuration settings for endpoints in sensor policy settings Organize sensors using sensor groups to assign the desired policy based on specific criteria Compare VDI sensor settings as compared to traditional endpoint sensor settings Determine requirements for the initial deployment of sensors Evaluate the policy impact on sensors Identify best practices for deploying sensors Installing Sensors Describe how to send an installation request Recognize the features and limitations of an installation code and company code Recognize the process for successfully completing an attended installation Recognize the differences between attended and unattended sensor installation methods Identify the correct deployment strategy for a given scenario Generate logs with unattended installations Generate sensor logs Check network connectivity for sensor installation Deploying Workloads Recognize the deployment process for VMware Carbon Black Cloud Workload Identify eligible workloads in a vSphere environment Recognize how to enable the VMware Carbon Black Cloud sensor on a VM workload Managing Sensors Describe VMware Carbon Black Cloud sensor deployment Explain the differences in sensor status Describe sensor update capabilities Explain sensor actions Manage vSphere workloads Post-deployment Validation Describe the process of a sensor background scan Recognize a properly registered sensor installation Identify sensor status in RepCLI Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Carbon Black Cloud: Plan and Deploy 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 VMware Carbon Black Cloud: Plan and Deploy 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 This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 5 Days 30 CPD hours This course is intended for System administrators and system integrators responsible for designing, implementing, and managing VMware Aria Automation Overview By the end of the course, you should be able to meet the following objectives: Describe the VMware Aria Automation architecture and use cases in cloud environments Describe the key services of VMware Cloud Automation Services™ Manage VMware Aria Automation entities on VMware and third-party virtual and cloud infrastructures Install VMware Aria Automation with VMware Aria Suite Lifecycle Configure and manage cloud accounts, projects, flavor mappings, image mappings, network profiles, storage profiles, volumes, tags, and services Create, modify, manage, and deploy VMware Aria Automation Templates Customize services and virtual machines with cloudConfig and cloudbase-init Configure and manage VMware Aria Automation Consumption Configure and manage ABX actions, custom properties, event broker subscriptions, and VMware Aria Automation Orchestrator workflows Connect to a Kubernetes cluster and manage namespaces Use VMware Aria Automation Config to configure and deploy systems Use logs and CLI commands to monitor and troubleshoot VMware Aria Automation During this five-day course, you focus on installing, configuring, and managing VMware Aria Automation 8.10? on-premises systems. You learn how it can be used to automate the delivery of virtual machines, applications, and personalized IT services across different data centers and hybrid cloud environments. The course covers how VMware Aria Automation Consumption? can aggregate content in native formats from multiple clouds and platforms into a common catalog.This course also covers interfacing VMware Aria Automation with other systems using VMware Aria Orchestrator and how to use VMware Aria Automation to manage Kubernetes systems and leverage other systems. In this course, you will use VMware Aria Automation Config? as a configuration management tool. Course Introduction Introductions and course logistics Course objectives VMware Aria Automation Overview and Architecture Describe the purpose and functionality of VMware Aria Automation Identify the key services offered by VMware Aria Automation Describe the VMware Aria Automation architecture Describe the use of VMware Workspace ONE Access? Describe the relationship between Kubernetes clusters, container, and VMware Aria Automation services Installing VMware Aria Automation List the different VMware Aria Automation deployment types Describe the purpose of Easy Installer Describe the VMware Aria Automation installation process Authentication and Authorization Identify the steps to integrating Workspace ONE© Access with Active Directory Describe the features of Workspace ONE Access Describe the user roles available in VMware Aria Automation Identify the key tasks performed by each user role Define custom roles Configure branding and multitenancy Basic Initial Configuration Create a basic configuration with a cloud account, cloud zone, project, flavor mapping, and image mapping VMware Aria Automation Templates Configure and deploy a basic VMware Aria Automation template Create a VMware Aria Automation template that can run on any cloud Use cloudConfig and cloudbase-init to run commands, create users, and install software Use YAML for inputs, variables, and conditional deployments Tags Configure tags Describe functions of tags Manage tags Storage Configuration Configure storage profiles Use tags and storage profiles Integrating NSX With VMware Aria Automation List the capabilities and use cases of VMware NSX© Describe the NSX architecture and components Integrate NSX with VMware Aria Automation List the supported network profiles in VMware Aria Automation Use the NSX components to design a multitier application with VMware Aria Automation Templates Identify the network and security options available in design canvas Create and manage on-demand networks and security groups Configure NSX Day 2 actions Integrating with Public Clouds Configure and use VMware Cloud Foundation? accounts Integrate VMware Cloud Director? account Configure and use an AWS cloud account Configure and use an Azure cloud account Configure and use a Google Cloud Platform cloud account Integrate VMware Cloud on AWS cloud account Using VMware Aria Automation Consumption Release a VMware Aria Automation template Define content source and content sharing Define VMware Aria Automation policy enforcement Use custom forms for catalog items VMware Aria Automation Extensibility Describe VMware Aria Automation extensibility Use event topics Create a subscription Call a VMware Aria Automation Orchestrator workflow Create ABX actions Using Kubernetes Clusters Introduction to Kubernetes Connect to an existing Kubernetes Cluster Create a VMware Aria Automation template with Kubernetes components Using VMware Aria Automation Config for Configuration Management Describe VMware Aria Automation Config Use VMware Aria Automation Config for software deployment Use VMware Aria Automation Config for configuration management Use VMware Aria Automation Config with event-driven orchestration VMware Aria Automation Troubleshooting and Integration Demonstrate how to monitor deployment history Demonstrate basic troubleshooting Execute CLI commands Explain how to collect logs Describe integration with VMware Aria Operations for Logs Describe integration with VMware Aria Operations

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Solutions architects, security DevOps, and security engineers Overview In this course, you will learn to: Establish a landing zone with AWS Control Tower Configure AWS Organizations to create a multi-account environment Implement identity management using AWS Single Sign-On users and groups Federate access using AWS SSO Enforce policies using prepackaged guardrails Centralize logging using AWS CloudTrail and AWS Config Enable cross-account security audits using AWS Identity and Access Management (IAM) Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and governance necessary to protect sensitive data and systems.In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices. Course Introduction Instructor introduction Learning objectives Course structure and objectives Course logistics and agenda Module 1: Governance at Scale Governance at scale focal points Business and Technical Challenges Module 2: Governance Automation Multi-account strategies, guidance, and architecture Environments for agility and governance at scale Governance with AWS Control Tower Use cases for governance at scale Module 3: Preventive Controls Enterprise environment challenges for developers AWS Service Catalog Resource creation Workflows for provisioning accounts Preventive cost and security governance Self-service with existing IT service management (ITSM) tools Module 4: Detective Controls Operations aspect of governance at scale Resource monitoring Configuration rules for auditing Operational insights Remediation Clean up accounts Module 5: Resources Explore additional resources for security governance at scale Additional course details: Nexus Humans AWS Security Governance at Scale 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 AWS Security Governance at Scale 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 5 Days 30 CPD hours This course is intended for Typical candidates for this course are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies Overview After you successfully complete this course, expect to be able to: Explain how Aruba's wireless networking solutions meet customers' requirements Explain fundamental WLAN technologies, RF concepts, and 802.11 Standards Learn to configure the Mobility Master and Mobility Controller to control access to the Employee and Guest WLAN Control secure access to the WLAN using Aruba Firewall Policies and Roles Recognize and explain Radio Frequency Bands and channels, and the standards used to regulate them Describe the concept of radio frequency coverage and interference and successful implementation and diagnosis of WLAN systems Identify and differentiate antenna technology options to ensure optimal coverage in various deployment scenarios Describe RF power technology including, signal strength, how it is measured and why it is critical in designing wireless networks Learn to configure and optimize Aruba ARM and Client Match features Learn how to perform network monitoring functions and troubleshooting AR-AMF teaches knowledge, skills & practical exp. to set up & config a basic AR WLAN utilizing OS 8.X architecture & features.using lecture & labs,AR-AMF provides tech. & hands-on exp. of config. a single Mobility Master with 1 controller & AP WLAN WLAN Fundamentals Describes the fundamentals of 802.11, RF frequencies and channels Explain RF Patterns and coverage including SNR Roaming Standards and QOS requirements Mobile First Architecture An introduction to Aruba Products including controller types and modes OS 8.X Architecture and features License types and distribution Mobility Master Mobility Controller Configuration Understanding Groups and Subgroups Different methods to join MC with MM Understanding Hierarchical Configuration Secure WLAN configuration Identifying WLAN requirements such as SSID name, encryption, authentication Explain AP groups structure and profiles Configuration of WLAN using the Mobility Master GUI AP Provisioning Describes the communication between AP and Mobility controller Explain the AP booting sequence and requirements Explores the APs controller discovery mechanisms Explains how to secure AP to controller communication using CPSec Describes AP provisioning and operations WLAN Security Describes the 802.11 discovery, authentication and association Explores the various authentication methods, 802.1x with WPA/WPA2, Mac auth Describes the authentication server communication Explains symmetric vs asymmetric Keys, encryption methods WIPS is described along with rogue discovery and protection Firewall Roles and Policies An introduction into Firewall Roles and policies Explains Aruba?s Identity based Firewall Configuration of Policies and Rules including aliases Explains how to assign Roles to users Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Guest Access Introduces Aruba?s solutions for Guest Access and the Captive portal process Configuration of secure guest access using the internal Captive portal The configuration of Captive portal using Clearpass and its benefits Creating a guest provisioning account Troubleshooting guest access Network Monitoring and Troubleshooting Using the MM dashboard to monitor and diagnose client, WLAN and AP issues Traffic analysis using APPrf with filtering capabilities A view of Airwaves capabilities for monitoring and diagnosing client, WLAN and AP issues

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.