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Duration 2 Days 12 CPD hours This course is intended for Develop for experienced IT Professionals familiar with Citrix Virtual Apps and Desktops 7.1x in an on-premises environment. Potential students include administrators, engineers, and architects designing or deploying Citrix Virtual Apps and Desktops workloads on Microsoft Azure. Overview Prepare the Azure environment for secure integration with Citrix Virtual Apps and Desktops Deploy and manage Virtual Delivery Agent machines in Microsoft Azure using Machine Creation Services Integrate Citrix Cloud and Citrix Virtual Apps and Desktops with Microsoft Azure Active Directory Design Machine Catalogs and virtual machines on Microsoft Azure Resource Manager Provide remote access with Citrix StoreFront and Citrix Gateway on Microsoft Azur Students learn to deploy and manage the Citrix Virtual Apps and Desktops apps and desktops in Microsoft Azure. Students gain the skills to plan the machine catalog and virtual machine design based in Microsoft?s public cloud and get hands-on practice deploying those machines using Machine Creation Services. Students will also learn about additional Azure considerations including maintenance and power management which are critical in a cloud environment. For remote access, students will learn to configure Citrix StoreFront and Citrix Gateway on the Azure platform. This course focuses on Microsoft Azure as a Citrix Cloud resource location however concepts are relevant to both Citrix Cloud and fully managed Citrix Virtual Apps and Desktops sites. Citrix Virtual Apps and Desktops on Azure Overview Defining IAAS Citrix Virtual Apps and Desktops Azure Deployment Models Azure Fundamentals Review Azure Management Azure Locations Citrix Virtual Apps and Desktops Integration with Azure Active Directory Active Directory Basics Active Directory Usage Connecting On-premises Active Directory to Azure Azure Role Based Access Control Connecting to Microsoft Azure Azure Connectivity Cloud Connectors in Azure Creating Citrix Virtual Apps and Desktops Host Connections to Azure Deploying Apps and Desktops using Machine Creation Services Master Image Preparation Machine Creation Services in Azure Considerations for Deploying onto Azure Providing Access to End Users StoreFront Locations Citrix ADC Locations Multiple Citrix Virtual Apps and Desktops Zones in Azure Regions Maintaining Infrastructure and VDAs in Microsoft Azure Maintaining Infrastructure Maintaining Resources Power Management Plan for a Successful POC Planning your next steps

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 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim

Duration 2 Days 12 CPD hours This course is intended for This course is recommended for administrators and engineers. Overview What you'll learn: Prepare the Azure environment for secure integration with Citrix DaaS. Deploy and manage Virtual Delivery Agent machines in Microsoft Azure using Machine Creation Services. Integrate Citrix Cloud and Citrix DaaS with Microsoft Azure Active Directory. Design Machine Catalogs and virtual machines on Microsoft Azure Resource Manager. Provide remote access with Citrix StoreFront and Citrix Gateway on Microsoft Azure. In this course you willLearn to deploy and manage your Citrix DaaS deployment on Microsoft Azure. Gain the skills to plan your machine catalog and virtual machine design based in Microsoft's public cloud and get hands-on practice deploying those machines using Machine Creation Services. You will also learn about additional Azure considerations including maintenance and power management which are critical in a cloud environment. For remote access, you will learn to configure Citrix StoreFront and Citrix Gateway on the Azure platform. This course focuses on Microsoft Azure as a Citrix Cloud resource location however concepts are relevant to both Citrix Cloud and fully managed Citrix DaaS sites. Module 1: Introduction to Citrix DaaS on Microsoft Azure Partnering for Success Module 2: Planning - Citrix DaaS Resource Location on Microsoft Azure Overview of Citrix DaaS Components Creating a Citrix DaaS Deployment Overview Module 3: Planning - Microsoft Azure Overview Azure Virtual Network Structure Azure Virtual Network Connectivity Azure Virtual Resources Azure Active Directory Identity and Access Management Azure Active Directory Options and Considerations Module 4: Planning - Deploying Citrix DaaS on Microsoft Azure Citrix DaaS Resource Locations in Azure Citrix DaaS Components in Azure Creating and Managing Workloads in an Azure Resource Location Module 5: Planning - Provide Access to End Users Providing Access to Resources in Citrix Cloud Citrix Gateway Deployment Options Deploying Citrix Gateway or ADC in Azure GSLB and StoreFront Optimal Gateway in Hybrid Environments Module 6: Rollout - Citrix DaaS Deployment on Microsoft Azure Citrix Workspace App Rollout Preparing Migration of End-Users to Workspace Platform Module 7: Managing - Citrix DaaS Workloads on Microsoft Azure Maintaining Citrix Gateway Backup and Monitoring in Azure Maintaining Master Images in Azure Monitoring VDAs in Manage Console and Azure Module 8: Optimize - Citrix DaaS on Microsoft Azure Managing Azure Costs Using Azure Pricing Calculator - Instructor Demo Student Exercise - Calculate a case Additional course details: Nexus Humans CWS-251 Implement Citrix DaaS on Microsoft Azure 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 CWS-251 Implement Citrix DaaS on Microsoft Azure 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 intended for information technology (IT) professionals who need to improve their expertise in Windows Server 2016 in the area of storage and compute functionality. Overview After completing this course, you will be able to: Configure and install Windows Server 2016 Manage Server Core, server upgrade and migration strategy Understand storage options Manage partition table formats Manage basic and dynamic disks, file systems Manage virtual hard disks, and drive hardware Manage disks and volumes Select and manage proper storage solutions for a specific scenario Storage Spaces and Data Deduplication implementation Configure and Manage Microsoft Hyper-V, virtual machines and Hyper-V containers Configure disaster recovery technologies Manage and Configure failover clustering for Hyper-V virtual machines Configure, plan and implement a Network Load Balancing (NLB) Work with deployment images This course is intended for information technology (IT) professionals who have basic knowledge of Windows Server. It is designed for professionals who have primary responsibility of managing storage and computing by using Windows Server 2016. Professionals who need to understand the scenarios, requirements, and storage and compute options that are available and applicable to Windows Server 2016.This course covers content that was in retired Microsoft Course 20740. Module 1: Installing, upgrading, and migrating servers and workloads Introducing Windows Server 2016 Preparing and installing Server Core Preparing for upgrades and migrations Migrating server roles and workloads Windows Server activation models Module 2: Configuring local storage Managing disks in Windows Server Managing volumes in Windows Server Module 3: Implementing enterprise storage solutions Overview of DAS, NAS, and SANs Comparing Fibre Channel, iSCSI, and Fibre Channel over Ethernet Understanding iSNS, DCB, and MPIO Configuring sharing in Windows Server 2016 Module 4: Implementing Storage Spaces and Data Deduplication Implementing Storage Spaces Managing Storage Spaces Implementing Data Deduplication Module 5: Installing and configuring Hyper-V and virtual machines Overview of Hyper-V Installing Hyper-V Configuring storage on Hyper-V host servers Configuring networking on Hyper-V host servers Configuring Hyper-V virtual machines Managing virtual machines Module 6: Deploying and managing Windows and Hyper-V containers Overview of containers in Windows Server 2016 Preparing for containers Installing, configuring, and managing containers by using Docker Module 7: Overview of high availability and disaster recovery Defining levels of availability Planning high availability and disaster recovery solutions with Hyper-V virtual machines Backing up and restoring by using Windows Server Backup High Availability with failover clustering in Windows Server 2016 Module 8: Implementing failover clustering Planning a failover cluster Creating and configuring a new failover cluster Maintaining a failover cluster Troubleshooting a failover cluster Implementing site high availability with stretch clustering Module 9: Implementing failover clustering with Windows Server 2016 Hyper-V Overview of yhe integration of Hyper-V Server 2016 with failover clustering Implementing Hyper-V VMs on failover clusters Key features for VMs in a clustered environment Module 10: Implementing Network Load Balancing Overview of NLB Configuring an NLB cluster Planning an NLB implementation Module 11: Creating and managing deployment images Introduction to deployment images Creating and managing deployment images by using MDT Virtual machine environments for different workloads Module 12: Managing, monitoring, and maintaining virtual machine installations WSUS overview and deployment options Update management process with WSUS Overview of Windows PowerShell DSC Overview of Windows Server 2016 monitoring tools Using Performance Monitor Monitoring event logs Additional course details: Nexus Humans 55324 Installation, Storage and Compute with Windows Server 2016 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 55324 Installation, Storage and Compute with Windows Server 2016 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 intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 5 Days 30 CPD hours This course is intended for System administrators System engineers Overview By the end of the course, you should be able to meet the following objectives: Install and configure ESXi hosts Deploy and configure vCenter Use the vSphere Client to create the vCenter inventory and assign roles to vCenter users Configure vCenter High Availability Create and configure virtual networks using vSphere standard switches and distributed switches Create and configure datastores using storage technologies supported by vSphere Use the vSphere Client to create virtual machines, templates, clones, and snapshots Configure and manage a VMware Tools Repository Create content libraries for managing templates and deploying virtual machines Manage virtual machine resource use Migrate virtual machines with vSphere vMotion and vSphere Storage vMotion Create and configure a vSphere cluster that is enabled with vSphere High Availability and vSphere Distributed Resource Scheduler Manage the life cycle of vSphere to keep vCenter, ESXi hosts, and virtual machines up to date Configure and manage vSphere networking and storage for a large and sophisticated enterprise Use host profiles to manage VMware ESXi host compliance Monitor the vCenter, ESXi, and VMs performance in the vSphere client This five-day, extended hour course takes you from introductory to advanced VMware vSphere© 8 management skills. Building on the installation and configuration content from our best-selling course, you will also develop advanced skills needed to manage and maintain a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you will install, configure, and manage vSphere 7. You will explore the features that build a foundation for a truly scalable infrastructure and discuss when and where these features have the greatest effect. This course prepares you to administer a vSphere infrastructure for an organization of any size using vSphere 8, which includes VMware ESXi? 8 and VMware vCenter Server© 8. Course Introduction Introductions and course logistics Course objectives vSphere and Virtualization Overview Explain basic virtualization concepts Describe how vSphere fits in the software-defined data center and the cloud infrastructure Recognize the user interfaces for accessing vSphere Explain how vSphere interacts with CPUs, memory, networks, storage, and GPUs Install an ESXi host vCenter Management Recognize ESXi hosts communication with vCenter Deploy vCenter Server Appliance Configure vCenter settings Use the vSphere Client to add and manage license keys Create and organize vCenter inventory objects Recognize the rules for applying vCenter permissions View vSphere tasks and events Create a vCenter backup schedule Recognize the importance of vCenter High Availability Explain how vCenter High Availability works Deploying Virtual Machines Create and provision VMs Explain the importance of VMware Tools Identify the files that make up a VM Recognize the components of a VM Navigate the vSphere Client and examine VM settings and options Modify VMs by dynamically increasing resources Create VM templates and deploy VMs from them Clone VMs Create customization specifications for guest operating systems Create local, published, and subscribed content libraries Deploy VMs from content libraries Manage multiple versions of VM templates in content libraries Configure and Manage vSphere Networking Configure and view standard switch configurations Configure and view distributed switch configurations Recognize the difference between standard switches and distributed switches Explain how to set networking policies on standard and distributed switches Configure and Manage vSphere Storage Recognize vSphere storage technologies Identify types of vSphere datastores Describe Fibre Channel components and addressing Describe iSCSI components and addressing Configure iSCSI storage on ESXi Create and manage VMFS datastores Configure and manage NFS datastores Discuss vSphere support for NVMe and iSER technologies Managing Virtual Machines Recognize the types of VM migrations that you can perform within a vCenter instance and across vCenter instances Migrate VMs using vSphere vMotion Describe the role of Enhanced vMotion Compatibility in migrations Migrate VMs using vSphere Storage vMotion Take a snapshot of a VM Manage, consolidate, and delete snapshots Describe CPU and memory concepts in relation to a virtualized environment Describe how VMs compete for resources Define CPU and memory shares, reservations, and limits Recognize the role of a VMware Tools Repository Configure a VMware Tools Repository Recognize the backup and restore solution for VMs 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 Deploying and Configuring vSphere Cluster Use Cluster Quickstart to enable vSphere cluster services and configure the cluster View information about a vSphere cluster Explain how vSphere DRS determines VM placement on hosts in the cluster Recognize use cases for vSphere DRS settings Monitor a vSphere DRS cluster Describe how vSphere HA responds to different types of failures Identify options for configuring network redundancy in a vSphere HA cluster Recognize the use cases for various vSphere HA settings Configure a cluster enabled for vSphere DRS and vSphere HA Recognize when to use vSphere Fault Tolerance Describe the function of the vCLS Recognize operations that might disrupt the healthy functioning of vCLS VMs ESXi Operations Use host profiles to manage ESXi configuration compliance Recognize the benefits of using configuration profiles Managing the vSphere Lifecycle Generate vCenter interoperability reports Recognize features of vSphere Lifecycle Manager Describe ESXi images and image depots Enable vSphere Lifecycle Manager in a vSphere cluster Validate ESXi host compliance against a cluster image and remediate ESXi hosts using vSphere Lifecycle Manager Describe vSphere Lifecycle Manager automatic recommendations Use vSphere Lifecycle Manager to upgrade VMware Tools and VM hardware Network Operations Configure and manage vSphere distributed switches Describe how VMware vSphere Network I/O Control enhances performance Define vSphere Distributed Services Engine Describe the use cases and benefits of vSphere Distributed Services Engine Storage Operations 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

Duration 5 Days 30 CPD hours This course is intended for System architects and system administrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of common vSphere log files Identify networking issues based on reported symptoms Validate and troubleshoot the reported networking issue Identify the root cause of networking issue Implement the appropriate resolution to recover from networking problems Analyze storage failure scenarios using a logical troubleshooting methodology identify the root cause of storage failure Apply the appropriate resolution to resolve storage failure problems Troubleshoot vSphere cluster failure scenarios Analyze possible vSphere cluster failure causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXiTM host and VMware vCenter problems Analyze failure scenarios of ESXi host and vCenter problems Select the correct resolution for the failure of ESXi host and vCenter problems Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day training course provides you with the knowledge, skills, and abilities to achieve competence in troubleshooting the VMware vSphere© 8 environment. This course increases your skill level and competence in using the command-line interface, VMware vSphere© Client?, log files, and other tools to analyze and solve problems. Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Discuss the various methods to run commands Discuss the various ways to access ESXi Shell Use commands to view, configure, and manage your vSphere components Use the vSphere CLI Use ESXCLI commands from the vSphere CLI Use Data Center CLI commands Identify the best tool for command-line interface troubleshooting Identify important log files for troubleshooting vCenter Server and ESXi Describe the benefits and capabilities of VMware SkylineTM Explain how VMware Skyline works Describe VMware SkylineTM Health Describe VMware Skyline AdvisorTM Troubleshooting Virtual Networking Analyze and troubleshoot standard switch problems Analyze and troubleshoot virtual machine connectivity problems Analyze and troubleshoot management network problems Analyze and troubleshoot distributed switch problems Troubleshooting Storage Discuss the vSphere storage architecture Identify the possible causes of problems in the various types of datastores Analyze the common storage connectivity and configuration problems Discuss the possible storage problems causes Solve the storage connectivity problems, correct misconfigurations, and restore LUN visibility Review vSphere storage architecture and functionality necessary to troubleshoot storage problems Use ESXi and Linux commands to troubleshoot storage problems Analyze log file entries to identify the root cause of storage problems Investigate ESXi storage issues Troubleshoot VM snapshots Troubleshoot storage performance problems Review multipathing Identify the common causes of missing paths, including PDL and APD conditions Solve the missing path problems between hosts and storage devices Troubleshooting vSphere Clusters Identify and troubleshoot vSphere HA problems Analyze and solve vSphere vMotion problems Diagnose and troubleshoot common vSphere DRS problems Troubleshooting Virtual Machines Discuss virtual machine files and disk content IDs Identify, analyze, and solve virtual machine snapshot problems Troubleshoot virtual machine power-on problems Identify possible causes and troubleshoot virtual machine connection state problems Diagnose and recover from VMware Tools installation failures Troubleshooting vCenter Server and ESXi Analyze and solve vCenter Server service problems Diagnose and troubleshoot vCenter Server database problems Use vCenter Server Appliance shell and the Bash shell to identify and solve problems Identify and troubleshoot ESXi host problems

Duration 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI