229 Microsoft Azure courses delivered Online

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for SharePoint administrators who work in a Windows network environment. At least one year of experience managing SharePoint servers and using PowerShell is recommended. Overview At Course Completion?Configure SharePoint Servers using PowerShell?Manage SharePoint Server Administration with PowerShell?Manage SharePoint Server Maintenance with PowerShell This course provides students with the knowledge and skills needed to use PowerShell to administer SharePoint Servers. Students will learn how to manage day-to-day and automated tasks carried out by SharePoint Administrators. Install and Configure SharePoint Servers with PowerShell Overview Preparing the Server Configure Server Features Configure Prerequisite Applications Install SharePoint Software Configure SharePoint Farm Review Lab 1: Configure SharePoint Servers with PowerShell Administering SharePoint Servers with PowerShell Overview Administering Users and Groups Administering the Farm Administering Shared Services & Features Administering Sites Administering Databases Review Lab 1: Administering SharePoint Servers with PowerShell Maintaining SharePoint Servers with PowerShell Overview Managing Backups / Restores Monitoring and Auditing Managing Notifications Scheduling Tasks Review Lab 1: Maintaining SharePoint Servers with PowerShell

Duration 5 Days 30 CPD hours This course is intended for This course is intended for administrators in a Microsoft-centric environment who want to build reusable units of automation, automate business processes, and enable less-technical colleagues to accomplish administrative tasks. Overview Describe the correct patterns for building modularized tools in Windows PowerShell Build highly modularized functions that comply with native PowerShell patterns Build controller scripts that expose user interfaces and automate business processes Manage data in a variety of formats Write automated tests for tools Debug tools This course covers advanced Windows PowerShell topics, with an emphasis on building reusable tools. Students are introduced to workflow, engage in best practices, and learn a variety of script development and toolmaking techniques. Tool Design Tools do one thing Tools are flexible Tools look native Start with a Command Why start with a command? Discovery and experimentation Build a Basic Function and Module Start with a basic function Create a script module Check prerequisites Run the new command Adding CmdletBinding and Parameterizing About CmdletBinding and common parameters Accepting pipeline input Mandatory-ness Parameter validation Parmeter aliases Emitting Objects as Output Assembling information Constructing and emitting output Quick tests An Interlude: Changing Your Approach Examining a script Critiquing a script Revising the script Using Verbose, Warning, and Informational Output Knowing the six channels Adding verbose and warning output Doing more with verbose output Informational output Comment-Based Help Where to put your help Getting started Going further with comment-based help Broken help Handling Errors Understanding errors and exceptions Bad handling Two reasons for exception handling Handling exceptions in our tool Capturing the actual exception Handling exceptions for non-commands Going further with exception handling Deprecated exception handling Basic Debugging Two kinds of bugs The ultimate goal of debugging Developing assumptions Write-Debug Set-PSBreakpoint The PowerShell ISE Going Deeper with Parameters Parameter positions Validation Multiple parameter sets Value from remaining arguments Help messages Aliases More CmdletBinding Writing Full Help External help Using PlatyPs Supporting online help ?About? topics Making your help updatable Unit Testing Your Code Sketching out the test Making something to test Expanding the test Going further with Pester Extending Output Types Understanding types The Extensible Type System Extending an object Using Update-TypeData Analyzing Your Script Performing a basic analysis Analyzing the analysis Publishing Your Tools Begin with a manifest Publishing to PowerShell Gallery Publishing to private repositories Basic Controllers: Automation Scripts and Menus Building a menu Using UIChoice Writing a process controller Proxy Functions A proxy example Creating the proxy base Modifying the proxy Adding or removing parameters Working with XML Data Simple: CliXML Importing native XML ConvertTo-XML Creating native XML from scratch Working with JSON Data Converting to JSON Converting from JSON Working with SQL Server Data SQL Server terminology and facts Connecting to the server and database Writing a query Running a query Invoke-SqlCmd Thinking about tool design patterns Design tools that use SQL Server for data storage Final Exam Lab problem Break down the problem Do the design Test the commands Code the tool

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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 IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to: Describe the functionality of Windows PowerShell and use it to run and find basic commands. Identify and run cmdlets for server administration. Work with Windows PowerShell pipeline. Describe the techniques Windows PowerShell pipeline uses. Use PSProviders and PSDrives to work with other forms of storage. Query system information by using WMI and CIM. Work with variables, arrays, and hash tables. Write basic scripts in Windows PowerShell. Write advanced scripts in Windows PowerShell. Administer remote computers. Use background jobs and scheduled jobs. Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 2 Days 12 CPD hours This course is intended for This course is intended for SQL Server administrators who work in a Windows network environment. At least one year of experience administering SQL Servers and using PowerShell is recommended. Overview After completing this course, students will be able to: •Install and Configure SQL Server and all its components using PowerShell •Manage day-to-day SQL Server Administration with PowerShell •Maintain the SQL Server environment using PowerShell scripting and programming options This course provides candidates with the knowledge and skills needed to use PowerShell for SQL Server administration. Students will learn how to manage day-to-day and scheduled maintenance tasks. Install and Configure SQL Server with PowerShell Install SQL Server Configure Database Settings Configure SQL Server Settings Configure SQL Server Policy-Based Management Lab 1: Configuring SQL Server with PowerShell Administering SQL Servers with PowerShell Managing Users and Roles Managing Database Performance Managing Database Availability Managing SQL Server Features Lab 1: Administering SQL Servers with PowerShell Maintaining SQL Server with PowerShell Managing Backups and Restores Maintaining Database Integrity and Performance Monitoring and Auditing Task Automation Generating Reports Lab 1: Maintaining SQL Servers with PowerShell

Duration 5 Days 30 CPD hours This course is intended for This course is intended for IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to:Describe the functionality of Windows PowerShell and use it to run and find basic commands.Identify and run cmdlets for server administration.Work with Windows PowerShell pipeline.Describe the techniques Windows PowerShell pipeline uses.Use PSProviders and PSDrives to work with other forms of storage.Query system information by using WMI and CIM.Work with variables, arrays, and hash tables.Write basic scripts in Windows PowerShell.Write advanced scripts in Windows PowerShell.Administer remote computers.Use background jobs and scheduled jobs.Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.

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