Oversikt over relevante kurs innen IT-kurs i Friesland

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert.   [-]

AZ-1008: Administer Active Directory Domain Services [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   AutoCAD P&ID grunnkurs Her er et utvalg av temaene du vil lære på kurset: Opprette og håndtere P&ID-prosjekter Tegne og redigere P&ID-diagrammer Eksportere og Importere data via Excel Uttrekk av lister og rapporter Du vil lære å opprette og håndtere P&ID-prosjekter, tegne diagrammer med de verktøy og kommandoer som er designet til formålet, og bruke toolpalettenes utstyr, ventiler, fittings og instrumenter. I tillegg til å bruke valideringsverktøyet til å kvalitetssikre ditt prosjekt, eksportfunksjonen til å skape 'nøytrale' AutoCAD-kopier av dine P&ID-tegninger samt å lage forskjellige rapporter og datauttrekk for f.eks. utstyr, ventiler, instrumenter osv.   Dette er et populært kurs, meld deg på nå!   Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

Power Pivot - Microsoft Excel [-]

Oracle Database: Introduction to SQL [-]

AZ-2005: Develop AI agents using Azure OpenAI and the Semantic Kernel SDK [-]

. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.   TARGET AUDIENCE Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam.   COURSE CONTENT Module 1: Creating Azure App Service Web Apps Students will learn how to build a web application on the Azure App Service platform. They will learn how the platform functions and how to create, configure, scale, secure, and deploy to the App Service platform. Azure App Service core concepts Creating an Azure App Service Web App Configuring and Monitoring App Service apps Scaling App Service apps Azure App Service staging environments Module 2: Implement Azure functions This module covers creating Functions apps, and how to integrate triggers and inputs/outputs in to the app. Azure Functions overview Developing Azure Functions Implement Durable Functions Module 3: Develop solutions that use blob storage Students will learn how Azure Blob storage works, how to manage data through the hot/cold/archive blob storage lifecycle, and how to use the Azure Blob storage client library to manage data and metadata. Azure Blob storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage Students will learn how Cosmos DB is structured and how data consistency is managed. Students will also learn how to create Cosmos DB accounts and create databases, containers, and items by using a mix of the Azure Portal and the .NET SDK. Azure Cosmos DB overview Azure Cosmos DB data structure Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions This module instructs students on how to use create VMs and container images to use in their solutions. It covers creating VMs, using ARM templates to automate resource deployment, create and manage Docker images, publishing an image to the Azure Container Registry, and running a container in Azure Container Instances. Provisioning VMs in Azure Create and deploy ARM templates Create container images for solutions Publish a container image to Azure Container Registry Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization Students will learn how to leverage the Microsoft Identity Platform v2.0 to manage authentication and access to resources. Students will also learn how to use the Microsoft Authentication Library and Microsoft Graph to authenticate a user and retrieve information stored in Azure, and how and when to use Shared Access Signatures. Microsoft Identity Platform v2.0 Authentication using the Microsoft Authentication Library Using Microsoft Graph Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions This module covers how to secure the information (keys, secrets, certificates) an application uses to access resources. It also covers securing application configuration information. Manage keys, secrets, and certificates by using the KeyVault API Implement Managed Identities for Azure resources Secure app configuration data by using Azure App Configuration Module 8: Implement API Management Students will learn how to publish APIs, create policies to manage information shared through the API, and to manage access to their APIs by using the Azure API Management service. API Management overview Defining policies for APIs Securing your APIs Module 9: Develop App Service Logic Apps This module teaches students how to use Azure Logic Apps to schedule, automate, and orchestrate tasks, business processes, workflows, and services across enterprises or organizations. Azure Logic Apps overview Creating custom connectors for Logic Apps Module 10: Develop event-based solutions Students will learn how to build applications with event-based architectures. Implement solutions that use Azure Event Grid Implement solutions that use Azure Event Hubs Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions Students will learn how to build applications with message-based architectures. Implement solutions that use Azure Service Bus Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions This module teaches students how to instrument their code for telemetry and how to analyze and troubleshoot their apps. Overview of monitoring in Azure Instrument an app for monitoring Analyzing and troubleshooting apps Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions Students will learn how to use different caching services to improve the performance of their apps. Develop for Azure Cache for Redis Develop for storage on CDNs [-]

Objectives This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data   All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introduction to Data Engineering -Explore the role of a data engineer-Analyze data engineering challenges-Intro to BigQuery-Data Lakes and Data Warehouses-Demo: Federated Queries with BigQuery-Transactional Databases vs Data Warehouses-Website Demo: Finding PII in your dataset with DLP API-Partner effectively with other data teams-Manage data access and governance-Build production-ready pipelines-Review GCP customer case study-Lab: Analyzing Data with BigQuery Module 2: Building a Data Lake -Introduction to Data Lakes-Data Storage and ETL options on GCP-Building a Data Lake using Cloud Storage-Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions-Securing Cloud Storage-Storing All Sorts of Data Types-Video Demo: Running federated queries on Parquet and ORC files in BigQuery-Cloud SQL as a relational Data Lake-Lab: Loading Taxi Data into Cloud SQL Module 3: Building a Data Warehouse -The modern data warehouse-Intro to BigQuery-Demo: Query TB+ of data in seconds-Getting Started-Loading Data-Video Demo: Querying Cloud SQL from BigQuery-Lab: Loading Data into BigQuery-Exploring Schemas-Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA-Schema Design-Nested and Repeated Fields-Demo: Nested and repeated fields in BigQuery-Lab: Working with JSON and Array data in BigQuery-Optimizing with Partitioning and Clustering-Demo: Partitioned and Clustered Tables in BigQuery-Preview: Transforming Batch and Streaming Data Module 4: Introduction to Building Batch Data Pipelines -EL, ELT, ETL-Quality considerations-How to carry out operations in BigQuery-Demo: ELT to improve data quality in BigQuery-Shortcomings-ETL to solve data quality issues Module 5: Executing Spark on Cloud Dataproc -The Hadoop ecosystem-Running Hadoop on Cloud Dataproc-GCS instead of HDFS-Optimizing Dataproc-Lab: Running Apache Spark jobs on Cloud Dataproc Module 6: Serverless Data Processing with Cloud Dataflow -Cloud Dataflow-Why customers value Dataflow-Dataflow Pipelines-Lab: A Simple Dataflow Pipeline (Python/Java)-Lab: MapReduce in Dataflow (Python/Java)-Lab: Side Inputs (Python/Java)-Dataflow Templates-Dataflow SQL Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer -Building Batch Data Pipelines visually with Cloud Data Fusion-Components-UI Overview-Building a Pipeline-Exploring Data using Wrangler-Lab: Building and executing a pipeline graph in Cloud Data Fusion-Orchestrating work between GCP services with Cloud Composer-Apache Airflow Environment-DAGs and Operators-Workflow Scheduling-Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, -Cloud Storage, and BigQuery-Monitoring and Logging-Lab: An Introduction to Cloud Composer Module 8: Introduction to Processing Streaming Data Processing Streaming Data Module 9: Serverless Messaging with Cloud Pub/Sub -Cloud Pub/Sub-Lab: Publish Streaming Data into Pub/Sub Module 10: Cloud Dataflow Streaming Features -Cloud Dataflow Streaming Features-Lab: Streaming Data Pipelines Module 11: High-Throughput BigQuery and Bigtable Streaming Features -BigQuery Streaming Features-Lab: Streaming Analytics and Dashboards-Cloud Bigtable-Lab: Streaming Data Pipelines into Bigtable Module 12: Advanced BigQuery Functionality and Performance -Analytic Window Functions-Using With Clauses-GIS Functions-Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz-Performance Considerations-Lab: Optimizing your BigQuery Queries for Performance-Optional Lab: Creating Date-Partitioned Tables in BigQuery Module 13: Introduction to Analytics and AI -What is AI?-From Ad-hoc Data Analysis to Data Driven Decisions-Options for ML models on GCP Module 14: Prebuilt ML model APIs for Unstructured Data -Unstructured Data is Hard-ML APIs for Enriching Data-Lab: Using the Natural Language API to Classify Unstructured Text Module 15: Big Data Analytics with Cloud AI Platform Notebooks -What’s a Notebook-BigQuery Magic and Ties to Pandas-Lab: BigQuery in Jupyter Labs on AI Platform Module 16: Production ML Pipelines with Kubeflow -Ways to do ML on GCP-Kubeflow-AI Hub-Lab: Running AI models on Kubeflow Module 17: Custom Model building with SQL in BigQuery ML -BigQuery ML for Quick Model Building-Demo: Train a model with BigQuery ML to predict NYC taxi fares-Supported Models-Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML-Lab Option 2: Movie Recommendations in BigQuery ML Module 18: Custom Model building with Cloud AutoML -Why Auto ML?-Auto ML Vision-Auto ML NLP-Auto ML Tables [-]

TARGET AUDIENCE Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C#, Python, or JavaScript and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure. COURSE OBJECTIVES After completing this course you should be able to: Describe considerations for creating AI-enabled applications Identify Azure services for AI application development Provision and consume cognitive services in Azure Manage cognitive services security Monitor cognitive services Use a cognitive services container Use the Text Analytics cognitive service to analyze text Use the Translator cognitive service to translate text Use the Speech cognitive service to recognize and synthesize speech Use the Speech cognitive service to translate speech Create a Language Understanding app Create a client application for Language Understanding Integrate Language Understanding and Speech Use QnA Maker to create a knowledge base Use a QnA knowledge base in an app or bot Use the Bot Framework SDK to create a bot Use the Bot Framework Composer to create a bot Use the Computer Vision service to analyze images Use Video Indexer to analyze videos Use the Custom Vision service to implement image classification Use the Custom Vision service to implement object detection Detect faces with the Computer Vision service Detect, analyze, and recognize faces with the Face service Use the Computer Vision service to read text in images and documents Use the Form Recognizer service to extract data from digital forms Create an intelligent search solution with Azure Cognitive Search Implement a custom skill in an Azure Cognitive Search enrichment pipeline Use Azure Cognitive Search to create a knowledge store   COURSE CONTENT Module 1: Introduction to AI on Azure Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you'll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for designing and implementing AI solutions responsibly. Introduction to Artificial Intelligence Artificial Intelligence in Azure Module 2: Developing AI Apps with Cognitive Services Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you'll learn how to provision, secure, monitor, and deploy cognitive services. Getting Started with Cognitive Services Using Cognitive Services for Enterprise Applications Lab: Get Started with Cognitive Services Lab: Get Started with Cognitive Services Lab: Monitor Cognitive Services Lab: Use a Cognitive Services Container Module 3: Getting Started with Natural Language Processing  Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text. Analyzing Text Translating Text Lab: Analyze Text Lab: Translate Text Module 4: Building Speech-Enabled Applications Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications. Speech Recognition and Synthesis Speech Translation Lab: Recognize and Synthesize Speech Lab: Translate Speech Module 5: Creating Language Understanding Solutions To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you'll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input. Creating a Language Understanding App Publishing and Using a Language Understanding App Using Language Understanding with Speech Lab: Create a Language Understanding App Lab: Create a Language Understanding Client Application Use the Speech and Language Understanding Services Module 6: Building a QnA Solution One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution. Creating a QnA Knowledge Base Publishing and Using a QnA Knowledge Base Lab: Create a QnA Solution Module 7: Conversational AI and the Azure Bot Service Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you'll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences. Bot Basics Implementing a Conversational Bot Lab: Create a Bot with the Bot Framework SDK Lab: Create a Bot with a Bot Freamwork Composer Module 8: Getting Started with Computer Vision Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video. Analyzing Images Analyzing Videos Lab: Analyse Images with Computer Vision Lab: Analyze Images with Video Indexer Module 9: Developing Custom Vision Solutions While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you'll explore the Custom Vision service, and how to use it to create custom image classification and object detection models. Image Classification Object Detection Lab: Classify Images with Custom Vision Lab: Detect Objects in Images with Custom Vision Module 10: Detecting, Analyzing, and Recognizing Faces Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user of cognitive services to identify human faces. Detecting Faces with the Computer Vision Service Using the Face Service Lab:Destect, Analyze and Recognize Faces Module 11: Reading Text in Images and Documents Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you'll explore cognitive services that can be used to detect and read text in images, documents, and forms. Reading text with the Computer Vision Service Extracting Information from Forms with the Form Recognizer service Lab: Read Text in IMages Lab: Extract Data from Forms Module 12: Creating a Knowledge Mining Solution Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights. Implementing an Intelligent Search Solution Developing Custom Skills for an Enrichment Pipeline Creating a Knowledge Store Lab: Create and Azure Cognitive Search Solution Create a Custom Skill for Azure Cognitive Search Create a Knowledge Store with Azure Cognitive Search   TEST CERTIFICATION Recommended as preparation for the following exams: AI-102 - Designing and Implementing a Microsoft Azure AI Solution - Part of the requirements for the Microsoft Certified Azure AI Engineer Associate Certification.   HVORFOR VELGE SG PARTNER AS:  Flest kurs med Startgaranti Rimeligste kurs Beste service og personlig oppfølgning Tilgang til opptak etter endt kurs Partner med flere av verdens beste kursleverandører [-]

MD-101: Managing Modern Desktops [-]

AI-3004: Build an Azure AI Vision solution with Azure AI services [-]

Test-Driven JavaScript - (Hands-on) [-]

Kursinnhold Enkle formler Cellereferanser Gi navn til celler og områder Feilkontroll og formelrevisjon Hente data fra andre ark og arbeidsbøker Egendefinerte tallformater Betinget formatering Utklippstavle og avansert innliming   Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer.   Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

RH124: Red Hat System Administration I - Linux (RHEL) [-]

Innhold: Bygge opp gode regnearkoppsett med formler, funksjoner og diagrammer. Summere flere regneark. Låse celler. Absolutt celle referanse, parenteser, hvis formler, Pivottabell. Kursholder Marianne Nylund er utdannet systemasvarlig/IKT-rådgiver fra forsvaret,Hun er sertifisert Microsoft-instruktør og har holdtMicrosoft Office-kurs siden 1998. Kursleder er tydelig, pedagogisk og flink til å forklare. Hun engasjerer sine kursdeltakere og gjør det underholdende å delta på våre kurs.Hun er meget tålmodig og tilpasser undervisningen etter hver enkelt deltagers behov, slik at alle skal få et stort utbytte av kursene.   [-]