Oversikt over relevante kurs innen IT-kurs i Aurskog Høland

Understand the purpose and key concepts of the Service Desk practice, including how it serves as the central point of contact between the service provider and the users, facilitating effective communication. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content mobil-optimised practical exercises     Exam: 20 questions Multiple Choice 30 minutes Closed book Minimum required score to pass: (65%)   [-]

Understand the purpose and key concepts of the Supplier Management Practice, elucidating its importance in managing supplier relationships and ensuring value delivery from third-party services. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Problem Management, including its role in identifying and managing the root causes of incidents to prevent recurrence.   This eLearning is: Interactive   Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Incident Management, including its role in restoring normal service operations swiftly following disruptions.   This eLearning is: Interactive Self-paced   Device-friendly   2-3 hour content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of the Continual Improvement Practice, elucidating its significance in fostering a culture of ongoing improvement and innovation within the organisation. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Release Management, elucidating its significance in planning, scheduling, and controlling the build, test, and deployment of releases to ensure they deliver the expected outcomes. The eLearning course: Interactive Self-paced Device-friendly 2-3 hour content Mobile-optimised Practical exercises   Exam:   20 questions Multiple choise Closed book 30 minutes Minimum required score to pass: 65% [-]

 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools After completing this module, you will be able to Provision an Azure Machine Learning workspace Use tools and code to work with Azure Machine Learning Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models After completing this module, you will be able to Run code-based experiments in an Azure Machine Learning workspace Train and register machine learning models Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets After completing this module, you will be able to Create and use environments Create and use compute targets Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing a Pipeline After completing this module, you will be able to Create pipelines to automate machine learning workflows Publish and run pipeline services Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service After completing this module, you will be able to Publish a model as a real-time inference service Publish a model as a batch inference service Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning After completing this module, you will be able to Optimize hyperparameters for model training Use automated machine learning to find the optimal model for your data Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]

COBIT 2019 Foundation [-]

Bli en mester i Cascading Style Sheets (CSS) med dette omfattende kurset fra Espen Faugstad hos Utdannet.no. Kurset "CSS: Komplett" tar deg fra grunnleggende til avanserte konsepter i CSS, som er avgjørende for moderne webutvikling. Uansett om du er nybegynner eller har litt forhåndskunnskap, vil kurset gi deg ferdighetene til å tilpasse eksisterende CSS-kode eller skrive din egen fra bunnen av. Utforsk hvordan du kan bruke typografi, farger, og former for å skape engasjerende og estetisk tiltalende brukeropplevelser. Lær om bruk av selektorer, boksmodellen, tekststilsetting, og skap moderne weboppsett med Flexbox og Grid. Kurset vil også guide deg gjennom å lage din egen portfolio-nettside, en praktisk anvendelse av dine nyinnlærte CSS-ferdigheter. Ved kursets slutt vil du ha en grundig forståelse av CSS og evnen til å anvende det effektivt i dine webprosjekter. Dette er Skandinavias mest omfattende kurs i CSS, og gir deg kompetansen til å skape attraktive muligheter i webutviklingsverdenen.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Kjernekompetanse Kapittel 3: Boksmodellen Kapittel 4: Tekst Kapittel 5: Utseende Kapittel 6: Float Kapittel 7: Positioning Kapittel 8: Flexbox Kapittel 9: Grid Kapittel 10: Responsiv design Kapittel 11: Resterende Kapittel 12: Avslutning   Varighet: 9 timer og 52 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. [-]

MB-220: Dynamics 365 Customer Insights - Journeys [-]

Har du noen gang lurt på hvordan du designer en avisartikkel? Dette kurset gir deg muligheten til å lære akkurat det. Kurset er strukturert som en workshop, med målet om at du skal kunne designe din egen avisartikkel når kurset er ferdig. Du har muligheten til å følge instruktøren eller velge din egen avis- eller magasinartikkel som utgangspunkt. Kurset er utviklet av Espen Faugstad, en autorisert Photoshop-ekspert. Du vil lære å tilpasse brukergrensesnittet i Adobe InDesign, opprette et dokument, legge til grafiske elementer og tekst. I tillegg vil du få opplæring i opprettelse, organisering og formatering av elementer som bilder, bildetekster, overskrifter, ingress og mer. En grunnleggende forståelse av InDesign er nødvendig for å dra nytte av dette kurset. Kapittel 1: Introduksjon Kapittel 2: Kladd Kapittel 3: Ferdigstille Kapittel 4: Avslutning Dette kurset gir deg praktiske ferdigheter innen avisdesign ved hjelp av Adobe InDesign, og gir deg muligheten til å utforske kreativiteten din innenfor dette området. Om du er en aspirerende designer eller bare nysgjerrig på hvordan avisdesign fungerer, er dette kurset for deg.   Varighet: 1 time og 34 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

Achieve More med MS Outlook (tidl. FTG) [-]

COURSE OVERVIEW   This Python Programming 1 course is designed to give delegates the knowledge to develop and maintain Python scripts using the current version (V3) of Python. There are many similarities between Python V2 and Python V3. The skills gained on this course will allow the delegate to develop their own skills further using Python V2 or V3 to support the development and maintenance of scripts. The Python Programming 1 course comprises sessions dealing with syntax,variables and data types,operators and expressions,conditions and loops,functions,objects,collections,modules and packages,strings,pattern matching,exception handling,binary and text files,and databases. Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered. TARGET AUDIENCE The Python Programming 1 course course is aimed at those who want to improve their Python programming skills,and for developers/engineers who want to migrate to Python from another language,particularly those with little or no object-oriented knowledge. For those wishing to learn Python and have no previous knowledge of programming,they should look to attend our foundation course Introduction to Programming - Python. COURSE OBJECTIVES This course aims to provide the delegate with the knowledge to be able to produce Python scripts and applications that exploit all core elements of the language including variables,expressions,selection and iteration,functions,objects,collections,strings,modules,pattern matching,exception handling,I/O,and classes. COURSE CONTENT DAY 1 COURSE INTRODUCTION Administration and Course Materials Course Structure and Agenda Delegate and Trainer Introductions SESSION 1: GETTING STARTED About Python Python versions Python documentation Python runtimes Installing Python The REPL shell Python editors SESSION 2: PYTHON SCRIPTS & SYNTAX Script naming Comments Docstring Statements The backslash Code blocks Whitespace Console IO (to enable the writing of simple programs) A first Python program Script execution SESSION 3: VARIABLES & DATA TYPES Literals Identifiers Assignment Numbers (bool,int,float,complex) Binary,octal,and hexadecimal numbers Floating point accuracy Collections (str,list,tuple,set,dict) None Implicit and explicit type conversion (casting) The type function SESSION 4: OPERATORS & EXPRESSIONS Arithmetic Operators Assignment Operators Comparison Operators Logical Operators Membership Operators Bitwise Operators Identity Operators SESSION 5: CONDITIONS & LOOPS Conditional statements (if,elif,else) Nested conditional statements Short hand if/if else Python's alternative to the ternary operator Iterative statements (while,for,else) The range function Iterating over a list Break Continue Nested conditional/iterative statements COURSE CONTENTS - DAY 2 SESSION 6: FUNCTIONS Declaration Invocation Default values for parameters Named arguments args and kwargs Returning multiple values None returned Variable scope Masking and shadowing The pass keyword Recursive functions SESSION 7: OBJECTS AND CLASSES About objects Attributes and the dot notation The dir function Dunder attributes Mutability The id function Pass by reference Introduction to Classes Class Declaration and Instantiation Data attributes Methods Composition SESSION 8: LISTS About lists List syntax including slicing Getting and setting list elements Iterating over a list Checking for the presence of a value The len function List methods incl. append,insert,remove,pop,clear,copy,sort,reverse etc. The del keyword Appending to and combining lists List comprehension SESSION 9: TUPLES About tuples Tuple syntax Getting tuple elements including unpacking Iterating over a tuple Checking for the presence of a value The len function Appending to and combining tuples SESSION 10: SETS About Sets Dictionary syntax Creating,adding and removing set elements Iterating over a set Membership Testing Sorting Copying Set methods incl. union,intersection,difference,symmetric_difference etc. COURSE CONTENTS - DAY 3 SESSION 11: DICTIONARIES About dictionaries Dictionary syntax Getting and setting dictionary elements Iterating over a dictionary (keys,values,and items) Checking for the presence of a key The len function Dictionary methods incl. keys,values,items,get,pop,popitem,clear etc. The del keyword Dictionary comprehension SESSION 12: STRINGS About strings String syntax including slicing Escape characters Triple-quoted strings Concatenation Placeholders The format method Other methods e.g. endswith,find,join,lower,replace,split,startswith,strip,upper etc. A string as a list of bytes SESSION 13: MODULES & PACKAGES About modules Inbuilt modules math,random and platform the dir() and help() functions Creating and using modules the __pycache__ and the .pyc files The module search path Importing modules Namespaces Importing module objects The import wildcard Aliases Importing within a function Executable modules Reloading a module About packages Importing packaged modules Importing packaged module objects Package initialisation Subpackages Referencing objects in sibling packages The Standard Library Installing modules and packages using pip SESSION 14: PATTERN MATCHING About regular expressions Regular expression special characters Raw strings About the re module re module functions incl. match,search,findall,full match,split,sub   COURSE CONTENTS - DAY 4 SESSION 15: EXCEPTION HANDLING About exceptions and exception handling Handling exceptions (try,except,else,finally) Exception types The exception object Raising exceptions Custom exception types Built-in exceptions hierarchy SESSION 16: FILES & THE FILESYSTEM The open function Methods for seeking (seekable,seek) Methods for reading from a file (readable,read,readline,readlines) Iterating over a file Methods for writing to a file (writable,write,writelines) Introduction to context managers Text encoding schemes,codepoints,codespace ASCII and UNICODE (UTF schemes) UTF-8,binary and hexadecimal representations The ord() and chr() functions Binary files,bytes and bytearray I/O layered abstraction. About the os module os module functions incl. getcwd,listdir,mkdir,chdir,remove,rmdir etc. OSError numbers and the errno module SESSION 17: DATABASES The DB-API DP-API implementations Establishing a connection Creating a cursor Executing a query Fetching results Transactions Inserting,updating,and deleting records FOLLOW ON COURSES Python Programming 2  Data Analysis Python  Apache Web Server PHP Programming  PHP & MySQL for Web Development  PHP & MariaDB for Web Development  Perl Programming  Ruby Programming  Introduction to MySQL  Introduction to MariaDB [-]

RH294: Red Hat System Administration III: Linux Automation with Ansible [-]

Excel Pivot kurs for deg som ønsker god oversikt over store datamengder. Gjennomgang av viktigheten av et korrekt grunnlag, for å kunne benytte de gode analysemulighetene som ligger i Excel. Det blir vist hvordan pivot kan brukes på forskjellige måter, og hvordan dette kan gjøres med dynamiske områder. Det vil også være rom for å demonstrere enkelte funksjoner som kan gjøre rapportering i Pivot bedre. Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Utgangspunktet: listen Få de beste rådene angående listen du skal bruke.   Utgangspunktet: tabellen Se fordeler ved å bruke dynamisk tabell vs. statisk liste.   Pivottabell - begrepsavklaringer Hva er en Pivottabell: Forklaring gis på ord og uttrykk relevant for pivottabellen. Grunnlaget: Grunnlaget, tabellen, bør være bygd opp på en spesiell måte, vi forklarer hvordan. Konsolidering: Vi viser hvordan grunnlaget kan være ulike krysstabeller som sys sammen til en pivottabell. Power Pivot Separate pivotminner: Hvorfor bruke dette?   Oppdatere pivottabell Endring i datakilden: Oppdatering av pivottabellen er viktig å kjenne til. Utvidelse av datakilden: Hva skjer dersom listen utvides enten i bredden eller i høyden. Dynamisk navngiving er en effektiv løsning. En annen metode er å opprette pivottabellen på basis av en liste som er definert som en tabell. Datakilder: Lær hvordan du kan ha flere ulike kilder som basis for pivottabellen, og hvordan disse skal oppdateres ved behov.   Pivottabellutseende Endring av oppsett: Lær hvor enkelt det er å endre oppsette for pivottabellen. Autooppsett: Excel 2010 har mange ulike autoformat. Formatering: I tillegg til formatet på selve pivottabellen, gjennomgår vi også celleformateringen. Sortering: Sorter gjerne tall og eller tekst Filter: Se forskjell på den tradisjonelle måten å filtrere på eller slicer. Skjule/vise: Sentralt når du ønsker å fokusere på deler av en rapport. Gruppering: Tekst, tall eller datoer kan fint grupperes. Nyttig! Vise / skjule delsummer: Praktisk å kjenne til hvordan du aktiverer / deaktiverer delsummer   Beregninger i pivottabell Bestemme ulike sammendrag: I en pivottabell kan du utføre ulike sammendrag. Egendefinert beregninger: Lær hvordan du kan lage nye beregnende felt på basis av eksisterende felt i en pivottabell. Vise data på ulike måter: Feltene kan vises blant annet som prosenter av andre felt, eller tallavvik av andre felt.   Anvendelse av pivottabellen Hent data: vi viser deg ulike metoder for å hente data fra en pivottabell, for bruk i ”vanlige” celler i Excel. Diagram: Lær hvordan du kan lage ulike diagrammer basert på data i pivottabellen   Power Pivot Import av PowerPivot data: Du kan hente data til PowerPivot vindu på ulike måter og fra ulike kilder. Koble data: Lær å opprette relasjoner mellom tabeller Rapporter: Bygg opp pivotrapporter fra relaterte grunnlag Beregninger: Introduksjon til DAX språket 4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti     [-]