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Duration 1 Days 6 CPD hours This course is intended for Software developers, system administrators, and IT professionals who are focused on Microsoft Windows Overview Configuring Microsoft Windows and Microsoft SQL Server in Google Compute Engine. Deploying ASP.NET MVC applications to Google Compute Engine. Deploying .NET Core applications to Google Compute Engine, Google Compute Engine, and Google Container Engine Learn how to create Windows virtual machines on Google Cloud so that you can deploy and run Microsoft Windows applications. In this course, you'll learn how to run SQL Server in Compute Engine, how to deploy instances across Google Cloud zones, and how to get more out of ASP.NET on Compute Engine, Google Container Engine, and App Engine. Introduction to Google Cloud Platform Scope and structure of GCP. Options for Windows deployment on GCP. GCP interfaces. Windows Workloads on Google Compute Engine Google Compute Engine virtual machine options. Integrating Active Directory with Google Compute Engine virtual machines. Options for running SQL Server in Google Compute Engine. Configuring SQL Server for high availability. Developing ASP.NET MVC applications Model-view-controller structure. Using Microsoft Visual Studio?s Web Project template to develop in ASP.NET. Deploying applications to Microsoft Internet Information Server (IIS) in GCE. Configuring Resilient Workloads Deploying instances across GCP zones. Using instance groups to create pools of virtual machines. Load balancing Windows applications. Delivering Next-Generation ASP.NET Core on GCP Understanding .NET Core and EF Core. Options for deploying ASP.NET Core applications on Google Cloud Platform. Deploying ASP.NET Core applications on Google Compute Engine. Deploying ASP.NET Core applications on Google Container Engine. Deploying ASP.NET Core applications on Google App Engine. Additional course details: Nexus Humans Develop and Deploy Windows Applications on Google Cloud Platform 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 Develop and Deploy Windows Applications on Google Cloud Platform 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 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 2 Days 12 CPD hours This course is intended for This course is designed for students wishing to gain intermediate-level skills or individuals whose job responsibilities include constructing relational databases and developing tables, queries, forms, and reports in Microsoft Access for Office 365. Overview In this course, you will optimize an Access database. You will: Provide input validation features to promote the entry of quality data into a database. Organize a database for efficiency and performance, and to maintain data integrity. Improve the usability of Access tables. Create advanced queries to join and summarize data. Use advanced formatting and controls to improve form presentation. Use advanced formatting and calculated fields to improve reports. In this course, you will expand your knowledge of relational database design; promote quality input from users; improve database efficiency and promote data integrity; and implement advanced features in tables, queries, forms, and reports. Extending your knowledge of Access will result in a robust, functional database for your users.This course covers Microsoft Office Specialist Program exam objectives to help you prepare for the Access Expert (Office 365 and Office 2019): Exam MO-500 certification. Improving Table Usability Topic A: Create Lookups Within a Table Topic B: Work with Subdatasheets Creating Advanced Queries Topic A: Create Query Joins Topic B: Create Subqueries Topic C: Summarize Data Improving Form Presentation Topic A: Apply Conditional Formatting Topic B: Create Tab Pages with Subforms and Other Controls Creating Advanced Reports Topic A: Apply Advanced Formatting to a Report Topic B: Add a Calculated Field to a Report Topic C: Control Pagination and Print Quality Topic D: Add a Chart to a Report Importing and Exporting Table Data Topic A: Import and Link Data Topic B: Export Data Topic C: Create a Mail Merge Using Queries to Manage Data Topic A: Create Action Queries Topic B: Create Unmatched and Duplicate Queries Creating Complex Reports and Forms Topic A: Create Subreports Topic B: Create a Navigation Form Topic C: Show Details in Subforms and Popup Forms Creating Access Macros Topic A: Create a Standalone Macro to Automate Repetitive Tasks Topic B: Create a Macro to Program a User Interface Component Topic C: Filter Records by Using a Condition Topic D: Create a Data Macro Using VBA to Extend Database Capabilities Topic A: Introduction to VBA Topic B: Using VBA with Form Controls Managing a Database Topic A: Back Up a Database Topic B: Manage Performance Issues Topic C: Document a Database Distributing and Securing a Database Topic A: Split a Database for Multiple-User Access Topic B: Implement Security Topic C: Convert an Access Database to an ACCDE File Topic D: Package a Database with a Digital Signature

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is as follows: Data Center engineers Cloud engineers System engineers Field engineers Implementation/operation/support/installation and upgrade specialists IT administrators Network engineers Cloud computing experts Security specialists Cisco integrators and partners Overview Upon completing this course, the learner will be able to meet these overall objectives: Describe items to be aware of before installing a Cisco CSR 1000V, including virtual machine requirements for installing Cisco CSR 1000V, licensing options that are available with Cisco CSR 1000V, supported Cisco IOS XE technologies, and management options for Cisco CSR 1000V. Prepare for installation of Cisco CSR 1000V Series routers. Explain common Cisco IOS XE Command-Line Interface (CLI) commands and conventions that can make it easier to work in the CLI, and describe how you can get help with command syntax and command options. Describe the options you can use to activate the license for Cisco CSR 1000V and activate the license for each option. Troubleshoot issues with Cisco CSR 1000V licenses. Upgrade the Cisco IOS XE software for an existing Cisco CSR 1000V installation. Describe the Call Home feature and its benefits, configure the feature on the Cisco CSR 1000V, including the anonymous reporting option, and display the Call Home configuration. List the different Call Home events that trigger alerts and commands that are executed as a result of the alert. Troubleshoot Cisco CSR 1000V Virtual Machine (VM) issues. Rehost a Cisco CSR 1000V license to a new VM, whether the current Cisco CSR 1000V router is accessible or not. Describe the Virtual Extensible LAN (VXLAN) Layer 2 gateway feature and configure this feature on the Cisco CSR 1000V router. Deploy the Cisco CSR 1000V in a virtual private cloud using Microsoft Azure or Amazon Web Services (AWS). Describe common network operations that the Cisco CSR 1000V supports. Explore programmability support on Cisco CSR 1000V, including APIs, shells, and data models. The Deploying Cloud Connect Solutions with Cisco Cloud Services Router 1000V (CLDCSR) course shows you how to deploy and operate Cisco© Cloud Services Router 1000V (CSR1000V) to provide comprehensive WAN gateway and network services functions including connectivity, routing, and security into virtual and cloud environments. Through expert instruction and hands-on labs, you will learn: Cisco CSR 1000V deployment options and requirements; hypervisor support, licensing models, features and programmability support; and how to implement, integrate, install, manage, and troubleshoot the deployment process and common operation issues.This class will help you:Learn how to use the CSR 1000V Series to extend your enterprise network to public and private cloudsGain hands-on practice acquiring skills in virtual and cloud-based technologies Course Outline Introducing Cisco CSR Product Overview Preparing for Installation Installing Cisco CSR 1000V in VMware Elastic Sky X (ESXi) Environments Booting Cisco CSR 1000V and Accessing the Console Using Cisco IOS XE Software Managing Cisco CSR 1000V Licenses Upgrading the Cisco IOS XE Software Mapping Cisco CSR 1000V Network Interfaces to Virtual Machine Network Interfaces Using GRUB Mode (Bootstrap Program) Configuring Call Home for Cisco CSR 1000V Configuring Virtual CPU (vCPU) Distribution Across Data, Control, and Service Planes Troubleshooting Cisco CSR 1000V Virtual Machine Issues Rehosting a Smart License Supporting the Cisco CSR 1000V Virtual Extensible LAN (VXLAN) Feature Deploying Cisco CSR 1000V in a Virtual Private Cloud Exploring Cisco CSR 1000V Operations Exploring Programmability on Cisco CSR 1000V

Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators, system engineers, and system integrators Overview By the end of the course, you should be able to meet the following objectives: Configure and manage a VMware Tools Repository Configure vSphere Replication and recover replicated VMs Manage VM resource usage with resource pools Configure and manage vSphere networking and storage for a large and sophisticated enterprise Configure vCenter High Availability Use host profiles to manage VMware ESXi host compliance Use the vSphere Client to manage certificates Monitor the vCenter, ESXi, and VMs performance in the vSphere client Secure vCenter, ESXi, and VMs in your vSphere environment Use VMware vSphere Trust Authority to secure the infrastructure for encrypted VMs Use Identity Federation to configure the vCenter to use external identity sources This five-day course teaches you advanced skills for configuring and maintaining a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you configure and optimize the VMware vSphere 8 features that build a foundation for a truly scalable infrastructure. You also discuss when and where these features have the greatest effect. Attend this course to deepen your understanding of vSphere and learn how its advanced features and controls can benefit your organization. Course Introduction Introductions and course logistics Course objectives Virtual Machine Operations Recognize the role of a VMware Tools Repository Configure a VMware Tools Repository Recognize the backup and restore solution for VMs Identify the components in the vSphere Replication architecture Deploy and configure vSphere Replication Recover replicated VMs vSphere Cluster Operations Create and manage resource pools in a cluster Describe how scalable shares work Describe the function of the vCLS Recognize operations that might disrupt the healthy functioning of vCLS VMs Network Operations Configure and manage vSphere distributed switches Describe how VMware vSphere Network I/O Control enhances performance Explain distributed switch features such as port mirroring and NetFlow Define vSphere Distributed Services Engine Describe the use cases and benefits of vSphere Distributed Services Engine Storage Operations Discuss vSphere support for NVMe and iSER technologies Describe the architecture and requirements of vSAN configuration Describe storage policy-based management Recognize components in the vSphere Virtual Volumes architecture Configure Storage I/O Control vCenter and ESXi Operations Create a vCenter backup schedule Recognize the importance of vCenter High Availability Explain how vCenter High Availability works Use host profiles to manage ESXi configuration compliance Use the vSphere client to manage vSphere certificates vSphere Monitoring Monitor the key factors that can affect a virtual machine's performance Describe the factors that influence vCenter performance Use vCenter tools to monitor resource use Create custom alarms in vCenter Describe the benefits and capabilities of VMware Skyline Recognize uses for Skyline Advisor Pro vSphere Security and Access Control Recognize strategies for securing vSphere components, such as vCenter, ESXi hosts, and virtual machines Describe vSphere support for security standards and protocols Describe identity federation and recognize its use cases Configure identity federation to allow vCenter to use an external identity provider vSphere Trusted Environments and VM Encryption Configure ESXi Host Access and Authentication Describe virtual machine security features Describe the components of a VM encryption architecture Create, manage, and migrate encrypted VMs List VM encryption events and alarms Describe the benefits and use cases of vSphere Trust Authority Configure vSphere Trust Authority

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

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 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 Experienced system administrators, system engineers, and system integrators Overview By the end of the course, you should be able to meet the following objectives: Configure and manage vSphere networking and storage for a large and sophisticated enterprise Use VMware vSphere Client™ to manage certificates Use Identity Federation to configure VMware vCenter Server to use external identity sources Use VMware vSphere Trust Authority™ to secure the infrastructure for encrypted VMs Use host profiles to manage VMware ESXi™ host compliance Create and manage a content library for deploying virtual machines Manage VM resource usage with resource pools Monitor and analyze key performance indicators for compute, storage, and networking resources for ESXi hosts Optimize the performance in the vSphere environment, including vCenter Server This five-day course teaches you advanced skills for configuring and maintaining a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you configure and optimize the VMware vSphere© 7 features that build a foundation for a truly scalable infrastructure, and you discuss when and where these features have the greatest effect. Attend this course to deepen your understanding of vSphere and learn how its advanced features and controls can benefit your organization. As an exclusive benefit, those who participate in this course will receive additional premium recorded lecture material on vSphere security. Course Introduction Introductions and course logistics Course objectives Network Scalability Configure and manage vSphere distributed switches Describe how VMware vSphere© Network I/O Control enhances performance Explain distributed switch features such as port mirroring and NetFlow Storage Scalability Explain why VMware vSphere© VMFS is a high-performance, scalable file system Explain VMware vSphere© Storage APIs - Array Integration, VMware vSphere© API for Storage Awareness?, and vSphere APIs for I/O filtering Configure and assign virtual machine storage policies Create VMware vSAN? storage policies Recognize components of the VMware vSphere© Virtual Volumes? architecture Configure VMware vSphere© Storage DRS? and VMware vSphere© Storage I/O Control Host and Management Scalability Use the vSphere Client to manage vSphere certificates Describe identity federation and recognize its use cases Configure identity federation to allow vCenter Server to use external identity provider Describe the benefits and use cases of vSphere Trust Authority Configure vSphere Trust Authority Use host profiles to manage ESXi configuration compliance Create a local content library and subscribe to a published content library Deploy VMs from a content library Create and manage resource pools in a cluster Describe how scalable shares work CPU Optimization Explain the CPU scheduler operation and other features that affect CPU performance Explain NUMA and vNUMA support Use esxtop to monitor key CPU performance metrics Memory Optimization Explain ballooning, memory compression, transparent page sharing, and host-swapping techniques for memory reclamation when memory is overcommitted Use esxtop to monitor key memory performance metrics Storage Optimization Describe storage queue types and other factors that affect storage performance Discuss vSphere support for NVMe and iSER technologies Use esxtop to monitor key storage performance metrics Network Optimization Explain performance features of network adapters Explain the performance features of vSphere networking Use esxtop to monitor key network performance metrics vCenter Server Performance Optimization Describe the factors that influence vCenter Server performance Use VMware vCenter© Server Appliance? tools to monitor resource use Supplemental Content Appendix A: vSphere Auto Deploy Explain the purpose of VMware vSphere© ESXi ? Image Builder CLI Explain the purpose of VMware vSphere© Auto Deploy? Describe how an autodeployed ESXi host boots Configure a vSphere Auto Deploy environment Appendix B: vSphere Security Configure ESXi Host Access and Authentication Recognize strategies for securing vSphere components, such as vCenter Server, ESXi hosts, and virtual machines Describe vSphere support for security standards and protocols Describe virtual machine security features Describe the components of a VM encryption architecture Create, manage, and migrate encrypted VMs Encrypt core dumps List VM encryption events and alarms

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) 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.