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DP-3007: Train and deploy a machine learning model with Azure Machine Learning [-]

Sliter du med å importere data og lage rapporter/analysegrunnlag i Excel? Et riktig grunnlag er avgjørende for en god rapport! Se hvor enkelt man lager rapporter og analyserer data med Pivottabeller. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Import av tekstfil med problemer   Bruk veiviser for å importere tekstfil Fiks ulike problemstilling allerede i veiviser   Kort repetisjon av prinsipper om å arbeide med lister   Datavask - opprydding i grunnlaget Lær ulike metoder for å rydde opp i grunnlaget   Tabellfunksjonalitet Formater liste som tabell Bli kjent med fordeler å jobbe mot tabell Bli kjent med strukturere cellereferanser   Pivotteknikk [-]

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.   Revit MEP MagiCAD VVS Videregående   Her er et utvalg av temaene du vil lære på kurset: Import av IFC-fil fra ArchiCADOppsett av prosjekt basert på IFC-fil (Levels, Grids, Spaces, georeferering)Lage egne produkter med MagiCAD-informasjon og Revit-familierSamspill mellom MagiCAD-produkter og Revit-familierTFM-merkesystemBeregninger: Dimensjonering, utbalansering, lydIFC: Property Set Manager, eksportkonfigurasjon, evaluering i Solibri   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   [-]

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 [-]

Sammenligne tegninger, også i batch Hvordan standardisere designgjennomgangen? Opprette tilpassede markeringsverktøy i Tool Chest Bruk av Markeringslisten for sporing, kommentering og status på markeringer Samhandling i sanntid mellom forskjellige aktører under designgjennomgangen i Studio Sessions [-]

Vil du komme igang med Photoshop? På dette kurset lærer du å korrigere farger og kontraster i bilder for å oppnå en bedre kvalitet. Du lærer å retusjere bort uønskede elementer og fjerne rynker. Du lærer å sammenkopiere flere elementer ved hjelp av lag og masker og lagre bilder til ulike medier, med ulik oppløsning og ulike filformater. Når du har vært gjennom dette Photoshop-kurset kan du bearbeide din egen eller andres ide fra skisse til et ferdig bilde. Du kan lagre bildet til det mediet det skal brukes i med høy kvalitet. Dette lærer du: Fjerne bakgrunnen i bildene Fargekorrigering slik at du får spennende og fine bilder Retusjering Sette sammen flere bilder Bruk av masker Jobbe med tekst og form Lagre bilder til ulike medier, med ulik oppløsning og ulike filformater Optimalisering av bilder til web https://igm.no/photoshop-kurs/ [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

AZ-140: Configuring and Operating Microsoft Azure Virtual Desktop [-]

COURSE OVERVIEW In this course, you will master the skills and technologies you need to implement core Cisco security solutions to provide advanced threat protection against cybersecurity attacks. You will learn security for networks, cloud and content, endpoint protection, secure network access, visibility and enforcements. You will get extensive hands-on experience deploying Cisco Firepower Next-Generation Firewall and Cisco ASA Firewall; configuring access control policies, mail policies, and 802.1X Authentication; and more.  You will get introductory practice on Cisco Stealthwatch Enterprise and Cisco Stealthwatch Cloud threat detection features. Please note that this course is a combination of Instructor-Led and Self-Paced Study - 5 days in the classroom and approx 3 days of self study. The self-study content will be provided as part of the digital courseware that you will recieve at the beginning of the course and should be part of your preparation for the exam. TARGET AUDIENCE Security individuals who need to be able to implement and operate core security technologies including network security, cloud security, content security, endpoint protection and detection, secure network access, visibility and enforcements. COURSE OBJECTIVES After completing this course you should be able to: Describe information security concepts and strategies within the network Describe common TCP/IP, network application, and endpoint attacks Describe how various network security technologies work together to guard against attacks Implement access control on Cisco ASA appliance and Cisco Firepower Next-Generation Firewall Describe and implement basic email content security features and functions provided by Cisco Email Security Appliance Describe and implement web content security features and functions provided by Cisco Web Security Appliance Describe Cisco Umbrella security capabilities, deployment models, policy management, and Investigate console Introduce VPNs and describe cryptography solutions and algorithms Describe Cisco secure site-to-site connectivity solutions and explain how to deploy Cisco IOS VTI-based point-to-point IPsec VPNs, and point-to-point IPsec VPN on the Cisco ASA and Cisco FirePower NGFW Describe and deploy Cisco secure remote access connectivity solutions and describe how to configure 802.1X and EAP authentication Provide basic understanding of endpoint security and describe AMP for Endpoints architecture and basic features Examine various defenses on Cisco devices that protect the control and management plane Configure and verify Cisco IOS Software Layer 2 and Layer 3 Data Plane Controls Describe Cisco Stealthwatch Enterprise and Stealthwatch Cloud solutions Describe basics of cloud computing and common cloud attacks and how to secure cloud environment   COURSE CONTENT Describing Information Security Concepts (Self-Study) Information Security Overview Managing Risk Vulnerability Assessment Understanding CVSS Describing Common TCP/IP Attacks (Self-Study) Legacy TCP/IP Vulnerabilities IP Vulnerabilities ICMP Vulnerabilities TCP Vulnerabilities UDP Vulnerabilities Attack Surface and Attack Vectors Reconnaissance Attacks Access Attacks Man-In-The-Middle Attacks Denial of Service and Distributed Denial of Service Attacks Reflection and Amplification Attacks Spoofing Attacks DHCP Attacks Describing Common Network Application Attacks (Self-Study) Password Attacks DNS-Based Attacks DNS Tunneling Web-Based Attacks HTTP 302 Cushioning Command Injections SQL Injections Cross-Site Scripting and Request Forgery Email-Based Attacks Describing Common Endpoint Attacks (Self-Study) Buffer Overflow Malware Reconnaissance Attack Gaining Access and Control Gaining Access via Social Engineering Gaining Access via Web-Based Attacks Exploit Kits and Rootkits Privilege Escalation Post-Exploitation Phase Angler Exploit Kit Describing Network Security Technologies Defense-in-Depth Strategy Defending Across the Attack Continuum Network Segmentation and Virtualization Overview Stateful Firewall Overview Security Intelligence Overview Threat Information Standardization Network-Based Malware Protection Overview IPS Overview Next Generation Firewall Overview Email Content Security Overview Web Content Security Overview Threat Analytic Systems Overview DNS Security Overview Authentication, Authorization, and Accounting Overview Identity and Access Management Overview Virtual Private Network Technology Overview Network Security Device Form Factors Overview Deploying Cisco ASA Firewall Cisco ASA Deployment Types Cisco ASA Interface Security Levels Cisco ASA Objects and Object Groups Network Address Translation Cisco ASA Interface ACLs Cisco ASA Global ACLs Cisco ASA Advanced Access Policies Cisco ASA High Availability Overview Deploying Cisco Firepower Next-Generation Firewall Cisco Firepower NGFW Deployments Cisco Firepower NGFW Packet Processing and Policies Cisco Firepower NGFW Objects Cisco Firepower NGFW NAT Cisco Firepower NGFW Prefilter Policies Cisco Firepower NGFW Access Control Policies Cisco Firepower NGFW Security Intelligence Cisco Firepower NGFW Discovery Policies Cisco Firepower NGFW IPS Policies Cisco Firepower NGFW Malware and File Policies Deploying Email Content Security Cisco Email Content Security Overview SMTP Overview Email Pipeline Overview Public and Private Listeners Host Access Table Overview Recipient Access Table Overview Mail Policies Overview Protection Against Spam and Graymail Anti-virus and Anti-malware Protection Outbreak Filters Content Filters Data Loss Prevention Email Encryption Deploying Web Content Security Cisco WSA Overview Deployment Options Network Users Authentication HTTPS Traffic Decryption Access Policies and Identification Profiles Acceptable Use Controls Settings Anti-Malware Protection Deploying Cisco Umbrella (Self-Study) Cisco Umbrella Architecture Deploying Cisco Umbrella Cisco Umbrella Roaming Client Managing Cisco Umbrella Cisco Umbrella Investigate Overview Explaining VPN Technologies and Cryptography VPN Definition VPN Types Secure Communication and Cryptographic Services Keys in Cryptography Public Key Infrastructure Introducing Cisco Secure Site-to-Site VPN Solutions Site-to-Site VPN Topologies IPsec VPN Overview IPsec Static Crypto Maps IPsec Static Virtual Tunnel Interface Dynamic Multipoint VPN Cisco IOS FlexVPN Deploying Cisco IOS VTI-Based Point-to-Point Cisco IOS VTIs Static VTI Point-to-Point IPsec IKEv2 VPN Configuration Deploying Point-to-Point IPsec VPNs on the Cisco ASA and Cisco Firepower NGFW Point-to-Point VPNs on the Cisco ASA and Cisco Firepower NGFW Cisco ASA Point-to-Point VPN Configuration Cisco Firepower NGFW Point-to-Point VPN Configuration Introducing Cisco Secure Remote Access VPN Solutions Remote Access VPN Components Remote Access VPN Technologies SSL Overview Deploying Remote Access SSL VPNs on the Cisco ASA and Cisco Firepower NGFW Remote Access Configuration Concepts Connection Profiles Group Policies Cisco ASA Remote Access VPN Configuration Cisco Firepower NGFW Remote Access VPN Configuration Explaining Cisco Secure Network Access Solutions Cisco Secure Network Access Cisco Secure Network Access Components AAA Role in Cisco Secure Network Access Solution Cisco Identity Services Engine Cisco TrustSec Describing 802.1X Authentication 802.1X and EAP EAP Methods Role of RADIUS in 802.1X Communications RADIUS Change of Authorization Configuring 802.1X Authentication Cisco Catalyst Switch 802.1X Configuration Cisco WLC 802.1X Configuration Cisco ISE 802.1X Configuration Supplicant 802.1x Configuration Cisco Central Web Authentication Describing Endpoint Security Technologies (Self-Study) Host-Based Personal Firewall Host-Based Anti-Virus Host-Based Intrusion Prevention System Application Whitelists and Blacklists Host-Based Malware Protection Sandboxing Overview File Integrity Checking Deploying Cisco AMP for Endpoints (Self-study) Cisco AMP for Endpoints Architecture Cisco AMP for Endpoints Engines Retrospective Security with Cisco AMP Cisco AMP Device and File Trajectory Managing Cisco AMP for Endpoints Introducing Network Infrastructure Protection (Self-Study) Identifying Network Device Planes Control Plane Security Controls Management Plane Security Controls Network Telemetry Layer 2 Data Plane Security Controls Layer 3 Data Plane Security Controls Deploying Control Plane Security Controls (Self-Study) Infrastructure ACLs Control Plane Policing Control Plane Protection Routing Protocol Security Deploying Layer 2 Data Plane Security Controls (Self-Study) Overview of Layer 2 Data Plane Security Controls VLAN-Based Attacks Mitigation STP Attacks Mitigation Port Security Private VLANs DHCP Snooping ARP Inspection Storm Control MACsec Encryption Deploying Layer 3 Data Plane Security Controls (Self-Study) Infrastructure Antispoofing ACLs Unicast Reverse Path Forwarding IP Source Guard Labs Configure Network Settings And NAT On Cisco ASA Configure Cisco ASA Access Control Policies Configure Cisco Firepower NGFW NAT Configure Cisco Firepower NGFW Access Control Policy Configure Cisco Firepower NGFW Discovery and IPS Policy Configure Cisco NGFW Malware and File Policy Configure Listener, HAT, and RAT on Cisco ESA Configure Mail Policies Configure Proxy Services, Authentication, and HTTPS Decryption Enforce Acceptable Use Control and Malware Protection Examine the Umbrella Dashboard Examine Cisco Umbrella Investigate Explore DNS Ransomware Protection by Cisco Umbrella Configure Static VTI Point-to-Point IPsec IKEv2 Tunnel Configure Point-to-Point VPN between the Cisco ASA and Cisco Firepower NGFW Configure Remote Access VPN on the Cisco Firepower NGFW Explore Cisco AMP for Endpoints Perform Endpoint Analysis Using AMP for Endpoints Console Explore File Ransomware Protection by Cisco AMP for Endpoints Console Explore Cisco Stealthwatch Enterprise v6.9.3 Explore CTA in Stealthwatch Enterprise v7.0 Explore the Cisco Cloudlock Dashboard and User Security Explore Cisco Cloudlock Application and Data Security Explore Cisco Stealthwatch Cloud Explore Stealthwatch Cloud Alert Settings, Watchlists, and Sensors TEST CERTIFICATION Recommended as preparation for the following exams: 350-701 - Implementing and Operating Cisco Security Core Technologies (SCOR 350-701)   This is the core exam for the Cisco CCNP Security certification, in order to gain the CCNP Security certification you will also need to pass one of the concentration exams. 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Bli en ekspert i online annonsering med Google Ads gjennom dette dyptgående kurset ledet av Espen Faugstad, gründer av Utdannet.no og en veteran med over 10 års erfaring i digital markedsføring. Dette kurset er skreddersydd for alle, fra de som aldri har brukt Google Ads før, til de som har erfaring men ønsker å heve sin kompetanse til ekspertnivå. Kurset starter med grunnleggende om hvordan du oppretter og konfigurerer en Google Ads-konto. Du vil lære å installere Google Ads-taggen og konverteringssporing, utføre målgruppe- og søkeordsanalyse, og forstå hvordan Google Ads-auksjonen fungerer. Kurset dekker også hvordan du oppretter og optimaliserer ulike typer annonser, inkludert tekst-, bilde-, video- og remarketingannonser. Med en praktisk tilnærming vil kurset guide deg gjennom prosessen med å sette opp effektive kampanjer, forstå auksjonssystemet, og bruke analyseverktøy for å forbedre dine resultater. Ved kursets slutt vil du ha tilegnet deg den kunnskapen du trenger for å mestre Google Ads og drive effektiv annonsering på vegne av deg selv eller dine klienter.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Målgruppe Kapittel 3: Søkeord Kapittel 4: Auksjon Kapittel 5: Tekstannonser Kapittel 6: Bildeannonser Kapittel 7: Videoannonser Kapittel 8: Remarketing Kapittel 9: Analyse Kapittel 10: Avslutning   Varighet: 5 timer og 12 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Lær å lage nettsider med WordPress Dette kurset passer for deg som skal designe eller bruke nettsider laget med WordPress og som trenger å forstå hvordan det virker. Du lærer også hvordan du forandrer og tilpasser designmaler, bruker utvidelser, knytter nettsiden mot sosiale medier, og hvordan du søkemotoroptimaliserer nettsidene slik at du blir funnet av brukerene. WordPress er en publiseringsløsning som gjør det enkelt å lage profesjonelle nettsider og det har den fordelen at kunden kan oppdatere og legge inn eget innhold. Dette er verdens største publiseringsverktøy og det finnes utallige designmaler som gjør det mulig for deg å endre utseende på nettsiden uten å endre innholdet. Dette lærer du: Bli kjent med løsningen Opprette nettsider på eget domene Du lærer å lage nettsider ved hjelp av ferdige maler Endre designmaler Publisere nettsider og innlegg Legge inn bilder Gjøre nettsiden din søkbar Bruk av plugins og widgets slik som f. eks: deling i sosiale medier, slideshow, kalenderfunksjon og mye mer Enkel bilde­redigering i WordPress https://igm.no/wordpress-kurs/ [-]

NTI leverer opplæring for å forenkle og effektivisere din arbeidshverdag Årlig utdanner over 8.000 personer seg i ulike CAD- og BIM-løsninger hos NTI.Vi har mer enn 70 forskjellige kurs innen fagområdene CAD/BIM-, Industri, Prosess, Plant og Infrastruktur- og dokumenthåndtering, og i snitt har våre 100 konsulenter og instruktører mer enn 10 års erfaring med opplæring og konsulenttjenester. Hvordan få riktig oppsett av koordinater i prosjekt? Dette er et tema NTI merker stor pågang rundt til support, og henvendelsene kommer fra disipliner som byggteknikk, VVS og elektro i tillegg til arkitekt. Det er ofte arkitekten som setter opp koordinatene i Revit. Hvis utgangspunktet er feil, påvirkes dette i alle andre disipliner også. Spesielt der det er krav til at utvekslingsformatet er IFC. På dette online-kurset vil du lære: Forskjellen mellom de ulike koordinatsystemene Hva er et lokalt nullpunkt Sette opp reelle koordinater (Survey) «Best Practice» i oppsett av koordinater fra start Samhandling ved utveksling av filer og koordinater Behandle flere koordinatsystemer i samme prosjekt IFC export/import i forhold til delte koordinater Det kan gå noe tid mellom hver gang du setter opp koordinater, og det er lett å glemme prosessen. Etter gjennomført kurs, får du en «step by step» dokumentasjon, som kan benyttes som oppslagsverk senere.  Kurs på dine betingelser!Ditt firma har kanskje investert i ny CAD-programvare, oppgradert til ny versjon, oppdatert til ny programvare eller dere trenger rett og slett oppfriskning. Da er det på tide å investere i kompetanse for dine ansatte! Kontakt vår kurskoordinator Wenche, telefon 21 40 27 89 eller epost. [-]

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 og LT Basis I Her er et utvalg av temaene du vil lære på kurset: AutoCADs brukergrensesnitt Den strategiske bruken av programmet fra start til slutt i tegningsprosessen Utføre og modifisere en tegning Bruk av tegneobjekter Bruk av redigeringsverktøy Laghåndtering, bruk av tekst og målsetting Håndtere layout og plotte den ferdige tegningen Etter kurset vil du ha god kjennskap til AutoCADs brukerflate og hvordan du skal bruke programmet gjennom hele tegneprosessen. Du vil kunne utføre og modifisere en tegning når det gjelder bruk av tegneobjekter, bruk av redigeringsverktøy, laghåndtering, tekstbruk og målsetning samt opprettelse og bruk av blokker. Du vil også kunne håndtere layout og plotte den ferdige tegningen. 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 [-]

Kursinnhold Flash Fill Diagrammer Sparkline grafikk Hurtiganalyse Sortering og filtrering Avansert filter Delsammendrag Tabeller Målgruppe Deg som Jobber med lister i Excel Ønsker å effektivisere databehandlingen i Excel Vil ha en kjapp gjennomgang av disse elementene. Har grunnleggende kunnskaper i Excel og ønsker å lære mer. Forkunnskaper Har laget regneark Har kunnskaper tilsvarende «Ta kontroll over regnearket» 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. [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: Øvinger: 8 av 12 må være godkjent. Personlig veileder: ja Vurderingsform: Praktisk hjemmeeksamen over 2 dager. Fra 09:00 til 15:00 dagen etter. Rapport leveres i itslearning. Ansvarlig: Stein Meisingseth Eksamensdato: 02.12.13 / 05.05.14         Læremål: Etter å ha gjennomført emnet Virtuelle Tjenere skal studenten ha følgende samlete læringsutbytte: KUNNSKAPER:Kandidaten:- ser fordeler, økonomiske og praktiske, ved å ta i bruk virtualiseringsteknologien til VMware- kjenner sentrale temaer innen drift av vSphere Infrastructure- forstår hvordan virtualisering er bygd opp FERDIGHETER:Kandidaten:- kan installere og konfigurere VMware vSphere- kan sette opp et cluster i vSphere vCenter- vise ut i fra rapporter gitt i vSphere Client om det trengs mer ressurser i opprettet cluster for dets kjørende virtuelle maskiner- forstår funksjonene vMotion, High Availability (HA) og Distributed Resource Scheduler (DRS)- kan automatisere enkle oppgaver ved bruk av PowerCLI script- kan utføre og- gjenopprette backup av virtuelle maskiner- kjenner til hvordan roller kan tildeles brukere GENERELL KOMPETANSE:Kandidaten:- har kompetanse til å besvare teoretiske problemstillinger innen virtualisering- har kompetanse til selvstendig både å ta i bruk sine kunnskaper og ferdigheter innen emnets tema i en driftssituasjon Innhold:Virtualisering med VMware.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Virtuelle Tjenere 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg   [-]