Må vi gemme en cookie?

Vi bruger cookies for at forbedre din oplevelse af vores hjemmeside, målrette indhold samt statistik. Læs mere om cookies
Accepter

Kurser

Kontakt
  1. Teknologisk Institut
  2. Kurser
  3. IT-kurser
  4. Machine Learning og Data Science
  5. Machine Learning
  6. Online kursus: Exploring The Future - VR, AI og Machine Learning
Kursusadministration

Brug for hjælp?

  • Gregersensvej 8
  • 2630 Taastrup
Google MapsApple MapsRejseplanen
  • Forskerparken Fyn, Forskerparken 10F
  • 5230 Odense M
Google MapsApple MapsRejseplanen
  • Teknologiparken Kongsvang Allé 29
  • 8000 Aarhus C
Google MapsApple MapsRejseplanen
  • NordsøcentretPostboks 104
  • 9850Hirtshals
Google MapsApple MapsRejseplanen
  • Gammel Ålbovej 1
  • 6092Sønder Stenderup
Google MapsApple MapsRejseplanen
Nyt
365 dages online kursus

Online kursus: Exploring The Future - VR, AI og Machine Learning

Få en forståelse for de teknologiske fremskridt, der er sket inden for kunstig intelligens, machine learning og virtual reality, og hvordan du kan udnytte disse teknologier i dit arbejdsliv. Kurserne er på engelsk og foregår online, når det passer dig. Du har adgang til online kurserne i 365 dage.

online kurser

Introduktion

Denne kursuspakke dækker de væsentligste aspekter af kunstig intelligens (AI), Machine Learning og Virtual Reality (VR). Du vil blandt andet lære at genkende forskellige former for kunstig intelligens, teknikker til at opbygge kunstigt intelligente systemer, samt at fejlfinde diverse problemer som kunstig intelligens og Machine Learning processer kan støde på. Derudover vil du få en introduktion til Virtual Reality, hvordan man designer Virtual Reality apps i Unity, GoogleVR og Unreal, samt hvilke problematikker man kan støde på i forbindelse med udviklingen af applikationer.
I kursuspakken finder du også kurser, der beskæftiger sig med Microservices og UI/UX.

Deltagerprofil

Kursuspakken henvender sig primært til softwareudviklere- og ingeniører og andre IT-professionelle, som er interesseret i, hvordan kunstig intelligens og Machine Learning har påvirket - og vil påvirke -området.

Alle med interesse for AI, Machine Learning og VR kan også have gavn af kurset.

Indhold

Exploring Artificial Intelligence

Introduction to Artificial Intelligence
  • Describe the four main definitions of artificial intelligence
  • Describe some of the fields in artificial intelligence research and their applications
  • List some of the techniques used to build artificial intelligence systems
  • Define intelligent agents
  • Describe the different types of intelligent agents
  • Define the task environment that intelligent agents live in
  • Distinguish between an observable, a partially observable, and an unobservable environment, and describe how these affect agents
  • Describe how the number of agents in a given environment can affect an agent
  • Define deterministic and stochastic environments and how the level of certainty in an environment affects agents
  • Describe the different types of environmental behavior and how this can affect agents
  • Create a description of an environment related to a particular problem and how an agent might behave in that environment
Search Problems
  • Define search problems and how these can be used by AI agents
  • List some problems that are ideal for searching algorithms
  • Define how to represent search problems
  • Describe the breadth-first search algorithm
  • Describe the depth-first search algorithm
  • Describe depth-limited search and the iterative deepening search algorithms
  • Describe the greedy approach for best-first informed searching
  • Define heuristics and their various properties
  • Describe how to create a good heuristic function for a given search problem
  • Describe the A* search algorithm
  • Describe local searching and the hill-climbing search algorithm
  • Describe the simulated annealing search algorithm and how it improves on hill-climbing search
  • Describe the three environmental characteristicsof search problems, state the function for a consistent heuristic, and state the function for an A* search
Constraint Satisfaction Problems
  • Define constraint satisfaction problems and describe how they are different from search problems
  • List some examples of problems that are better for constraint satisfaction algorithms than search algorithms
  • Describe how to use a backtracking search to solve a constraint satisfaction problem
  • Describe how to order variables when performing a backtracking search
  • Describe arc consistency and other types of constraint consistency in a constraint satisfaction problem
  • Describe how to use arc consistency to solve a constraint satisfaction problem with constraint propagation
  • Describe how to use the backjumping and forward checking inference method in a backtracking search
  • Describe how local search algorithms can be used to solve constraint satisfaction problems
  • Describe how to represent a Sudoku puzzle and how to solve it as a constraint satisfaction problem
  • Build a full high-level representation and solution for a constraint satisfaction problem
Adversarial Problems
  • Describe adversarial problems and the challenges they impose on AI
  • Specify how to represent an adversarial problem
  • Describe how to use the minimax algorithm to play an adversarial game and some of its shortcomings
  • Describe how to use alpha-beta pruning to improve the performance of the minimax algorithm
  • Describe evaluation functions
  • Describe how to use cutoffs to be able to perform adversarial searches under a time constraint
  • Describe how lookup tables can be used to improve an agent's performance
  • Describe chess and how agents can be made to play the game of chess
  • Describe expectiminimax values in stochastic games and how they make solution searching harder
  • Describe different evaluation functions that can be used to search in a stochastic game
  • Describe how to use monte carlo simulations to make decisions when searching
  • Build a full high-level representation and solution for an adversarial game using the minimax algorithm and alpha-beta pruning
Uncertainty
  • Describe uncertainty and how it applies to AI
  • Describe how probability theory is used to represent knowledge to help an intelligent make decisions
  • Describe utility theory and how an agent can calculate expected utility of decisions
  • Describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents
  • Describe how risks are taken into consideration when calculating utility and how attitude for risks can change the utility function
  • Describe the utility of information gain and how information gain can influence decisions
  • Define Markov chains
  • Define the Markov Decision Process and how it applies to AI
  • Describe the value iteration algorithm to decide on an optimal policy for a Markov Decision Process
  • Define the partially observable Markov Decision Process and contrast it with a regular Markov Decision Process
  • Describe how the value iteration algorithm is used with the partially observable Markov Decision Process
  • Describe how a partially observable Markov Decision Process can be implemented with an intelligent agent
  • Describe the Markov Decision Process and how it can be used by an intelligent agent
Machine Learning
  • Describe how AI learns and the different types of machine learning
  • describe how examples can be used for learning
  • Describe decision trees and how the model expresses knowledge
  • Describe entropy and information gain for learning decision tree models
  • Describe how to choose attributes to learn a decision tree
  • Describe overfitting and how decision tree models can be made to mitigate this issue
  • Describe neural networks and how they apply to artificial intelligence
  • Describe the structure of a neural network and its individual neurons
  • List some of the common types of neural networks and what problems they might be good at solving
  • Describe how machine learning works with a perceptron
  • Describe how perceptron learning can be generalized to a multilayered neural network
  • Describe convolutional neural networks
  • Describe recurrent neural networks
  • Describe how a perceptron can learn how to achieve a particular result given a set of examples
Reinforcement Learning
  • Describe reinforcement learning and list some of the techniques that agents can use to learn
  • Describe additive rewards and discounted rewards
  • Describe passive learning
  • Describe how to use direct utility estimation for passive learning and how to define the Bellman Equation in the context of reinforced learning
  • Describe temporal difference learning and contrast it with direct utility estimation
  • Describe active learning and contrast it with passive learning
  • Describe exploration and exploitation in the context of active reinforced learning and describe some of the exploration policies used in learning algorithms
  • Define Q-learning for reinforced learning
  • Describe the different parts used in Q-learning and how these can be implemented
  • Describe on-policy and off-policy learning and the difference between the two
  • describe why lookup tables aren't ideal for most reinforced learning tasks and how to build some function approximations that can make these problems possible
  • Describe how deep neural networks can be used to approximate q-value for given states in Q-learning
  • Describe Q-learning and how to set up the algorithm for a particular problem
Introducing Natural Language Processing
  • Define NLP, and list some of its applications and methods
  • Describe some of the base NLP operations such as Regex, tokenization, and stemming
  • Describe the porter stemming algorithm used to stem English text
  • Describe named entity recognition and some of the methods used to perform this task
  • Describe how NLP models are built and the various parts required to build them
  • Describe text classification, why it's useful, and how to perform it
  • Describe the Naïve Bayes classification algorithm and how it can be used as a simple text classification algorithm
  • Describe information retrieval and some of the base techniques used to perform this task
  • Describe how an AI agent can use simple information retrieval techniques to answer some simple questions
  • Describe parsing and how it can be accomplished using NLP
  • Describe the challenges related to machine translation and some of the methods used to accomplish this task
  • Describe methods used by computers to recognize speech
  • Describe many different types of operations that can be used with natural language processing

Exploring Microservices

Microservices Architecture
  • Define a service and its purpose in a service oriented architecture (SOA)
  • Identify microservices and its advantages
  • Distinguish the architecture behind microservices
  • Recognize various microservice processes
  • Demonstrate microservices from use cases
  • Identify various early versions of microservices
  • Define the monolithic approach and the differences of using monolithic over SOA
  • Recognize the benefits and costs of SOA
  • Distinguish concepts in implementations of SOA
  • Recognize early approaches including EAI and CORBA and SOAP
  • Describe the process of event-driven architecture with event sourcing
  • Describe the Command Query Responsibility Segregation and how to implement queries in a microservice architecture
  • Describe various design patterns for microservice-based architectures
  • Recognize SOA concepts with Dev Ops and continuous deployment and delivery
  • Define dependencies in microservices
  • Compare the differences between using modules and services
  • Compare the importance of cohesion and coupling microservices
  • Define the various communication processes including Direct Client-to-Microservices communication and API Gateway
  • Recognize concepts in building IPC
  • Recognize the various approaches to microservice architecture from past to present
Microservices Containers
  • Define Docker and describe its components and relation to the microservice architecture
  • Describe the benefits of using Docker with microservices
  • Install and configure Docker
  • Use the Docker GUI and toolbox
  • Define containers in microservices
  • Recognize how to use containers with microservices
  • Define Dockerfile and how the tool is used to build and automate actions
  • Use Docker to create a test database server
  • Create a process using Docker to automate several processes
  • Identify Kubernetes services
  • Describe processes in connecting applications with services
  • Configure a Kubernetes cluster
  • Recognize how to use Docker with microservices
Microservices Deployment and Continous Integration
  • Identify best practices in microservice deployment
  • Describe various deployment implementations
  • Recognize the use of service discovery and its benefits
  • Describe various service discovery patterns
  • Define Springboot and its use with microservices
  • Describe how to install Springboot
  • Define spring building with Maven and Gradle
  • Define various springboot auto configurations
  • Recognize concepts in REST and HTTP architecture
  • Define various common REST constraints
  • Define continuous integration and how its mapped to microservices
  • Describe the Jenkins interface using the Maven plugin
  • Describe the process of building and composing containers for the Jenkins UI
  • Recognize the process of testing continuous integration
  • List benefits of continues and performance testing
  • describe various best practices for designing continuous delivery pipelines
  • recognize the differences between continuous integration and continuous delivery
  • recognize deployment architecture and RESTful services
Microservices Testing Strategies, Scaling, Monitoring, and Security
  • Define the various microservices tests including unit and service testing
  • Define the scope of testing end to end
  • Recognize the use of performance and cross-functional testing
  • Describe the process in implementing service tests
  • Define microservices scaling and the various methods
  • Describe the process of scaling microservices databases
  • Recognize the process of caching microservices
  • Define autoscaling and how it's used
  • Define various microservices monitoring tools
  • Describe the process of monitoring a single server
  • Describe the process of monitoring several servers
  • Recognize various microservices logging tools
  • Define log management and concepts applied to microservices
  • Recognize the process of metric tracking across a span of servers
  • Recognize general security practices with microservices
  • Describe the processes of authentication and authorization
  • Describe the process of fine grain authorization
  • Describe service to service authentication and authorization
  • Recognize the process of testing and scaling microservices end to end

Exploring Machine Learning

Introduction to Machine Learning and Supervised Learning
  • Define machine learning and how it can be used to solve a variety of problems
  • Define supervised machine learning
  • Describe the fundamentals of building machine learning models to solve a problem
  • Describe overfitting, how it can be a problem, and how to mitigate it
  • Evaluate machine learning models and compare them
  • Define the linear regression model for one and multiple variable problems
  • Describe the gradient descent algorithm for training linear regression models
  • Describe the k-nearest neighbor model and how to learn it
  • Describe decision tree models and how to learn decision trees using the C4.5 algorithm
  • Set up scikit-learn for Python
  • Import data, and perform basic tasks with scikit-learn for Python
  • Use scikit-learn to fit a linear regression model to a dataset
  • Use scikit-learn's k-nearest neighbor model
  • Use scikit-learn to fit a decision tree model to a dataset
  • Use scikit-learn and GraphViz to generate a decision tree model from a dataset
  • Use scikit-learn to calculate the precision and the recall of different machine learning models in Python
  • Fit a linear regression model to a dataset with scikit-learn and Python
Supervised Learning Models
  • Describe the difference between classification and regression models and the use for each of them
  • Describe how decision trees can be applied to regression problems
  • Describe the CART decision tree learning algorithm and how it's different from C4.5
  • Describe the random forests machine learning
  • Use scikit-learn to build a random forest model in Python
  • Describe the logistic regression model
  • Use scikit-learn to fit a logistic regression model
  • Describe support vector machine models
  • Describe how to use kernel methods with support vector machines to model more complex data
  • Use scikit-learn to train and support vector machines in Python
  • Describe the Naïve Bayes classifiers and how to train them
  • Use scikit-learn to fit a Naïve Bayes classifier in Python
  • Describe different supervised learning models in Python
Unsupervised Learning
  • Describe unsupervised learning and some of the problems it can solve
  • Describe rule association and how the apriori algorithm performs this task
  • Use the apriori algorithm for rule association in Python
  • Describe clustering and the types of problems it applies to
  • Describe the k-means clustering algorithm
  • Use SciKit Learn to build clusters in python
  • Describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
  • Use scikit learn to perform anomaly detection
  • Describe the problems with dimensionality and why efforts to reduce dimensionality should be taken
  • Describe principal component analysis for dimensionality reduction
  • Use SciKit Learn to perform dimensionality reduction
  • Perform dimensionality reduction and clustering tasks in Python
Neural Networks
  • Describe neural networks and their capabilities
  • Describe how different neural networks are structured
  • Describe how cost functions are used to train neural networks
  • Describe activation functions and list different types of commonly used activation functions
  • Describe feedforward neural networks and the intuition behind calculating gradients in neural networks
  • Describe how to use backpropagation for more efficient neural network training
  • Describe batch learning and why it makes neural network training easier
  • Describe TensorFlow and its high-level architecture
  • Set up TensorFlow for use on a CPU
  • Import data into TensorFlow using built-in data sources and external data sources
  • Build and train a single-layer neural network in TensorFlow
  • Build and train a multilayer neural network in TensorFlow
  • Describe neural networks, network layers, cost functions, activation functions, and gradient descent
Convolutional and Recurrent Meural Networks
  • Describe the two main types of error in machine learning models and the tradeoff between them
  • Describe how to use cross-validation to show how generalized a model is
  • Describe cross-validation in Python to obtain strong evaluation scores
  • Describe different metrics that can be used to evaluate binary classification models
  • Describe different metrics that can be used to evaluate non-binary classification models
  • Describe common evaluation metrics for evaluating classification models
  • Describe different metrics that can be used to evaluate regression models
  • Describe how to use Python to calculate common evaluation methods
  • Describe AWS machine learning
  • Set up an AWS environment and import data sources
  • Create a model with AWS
  • Set training criteria with AWS and train a model
  • Define bias, variance, and tradeoffs
Applying Machine Learning
describe the two main types of error in machine learning models and the tradeoff between them
describe how to use cross-validation to show how generalized a model is
describe cross-validation in Python to obtain strong evaluation scores
describe different metrics that can be used to evaluate binary classification models
describe different metrics that can be used to evaluate non-binary classification models
describe common evaluation metrics for evaluating classification models
describe different metrics that can be used to evaluate regression models
describe how to use Python to calculate common evaluation methods
describe AWS machine learning
set up an AWS environment and import data sources
create a model with AWS
set training criteria with AWS and train a model
define bias, variance, and tradeoffs

Exploring the Future of UI/UX

Introduction to UI/UX
  • Describe UI/UX for software
  • Compare the differences between UI and UX
  • Describe why UI/UX is important for software products
  • Describe the future of UI/UX as new technologies rise in popularity
  • Describe how to prevent users from making errors
  • Use flow charts to make UI/UX designs
  • Use journey maps with UX design
  • Describe microinteractions and their importance
  • Describe age-responsive design and how it could enhance user experience
  • Define interstitial anxiety and how you should design UI/UX around it
  • Describe the differences with VR interfaces and normal interfaces
  • List modern concepts in UI/UX
Designing with Flexibility and Efficiency in Mind
  • Describe why and how to use design conventions for UI/UX
  • List guidelines to follow when doing UI/UX designs
  • Work with material design
  • Work with IOS design guidelines
  • Create better forms for your applications
  • Describe why security is an important consideration with UI/UX design
  • Create better password inputs to enhance security
  • Create information recovery systems for better security and user experience
  • Use modern CAPTCHA methods without being a burden to users
  • Use certain techniques to make users trust you
  • Describe effective UI/UX principles
Exploring Chatbots
  • Describe chatbots
  • Describe why chatbots are important
  • Describe how chatbots work
  • Create better user experiences with chatbots
  • Describe Facebook Messenger bots and their capabilities
  • Describe Twitter bots and their capabilities
  • Describe Slack bots and their capabilities
  • Describe Skype bots and their capabilities
  • Describe SMS bots and their capabilities
  • Set up an environment to develop messenger bots
  • Create and run messenger bots
  • Publish messenger bots for discovery
  • Create a messenger bot
Voice User Interfaces
  • Define voice user interfaces (VUIs)
  • Describe how UI/UX applies to voice interfaces
  • List design considerations for VUIs
  • Compare GUI and VUI in terms of design
  • Use different tools to design voice apps
  • List some of the most popular voice interface technologies
  • Work with the Alexa Skills Kit
  • Create voice skills for Alexa
  • Work with SiriKit
  • Work with voice actions on Google
  • Set up a voice app using your preferred voice interface
UI/UX for Chatbot and Voice Interface AI’s
  • Describe how UI/UX impacts AI design
  • Create a personality for your AI program that enhances user experience
  • Design methods to mitigate user errors
  • Design methods to make AI able to help you use the app
  • Create more natural conversations
  • Describe what API.AI is
  • Create an AI agent using a prebuilt agent
  • Add functionalities to API.AI agents
  • Deploy API.AI agents as Messenger bots
  • Describe wit.ai
  • Use wit.ai to create a simple AI agent
  • Describe the Microsoft Bot Framework
  • Create an api.ai agent
Wearable Technology and Unity VR
  • Describe wearable technology
  • Describe some design considerations involved with wearable devices and apps
  • Describe how to design smartwatch apps
  • Describe Android Wear and some of its core principles
  • List the core components of Android Wear
  • Describe some design patterns that Android Wear apps should use
  • Describe watchOS and some of it's core principles and components
  • Describe design principles for wearables
Internet of Things and UI/UX
  • Describe IoT and list some of its applications
  • List challenges related to IoT UI/UX design
  • Describe design principles used to design IoT devices
  • Describe sensors and actuators and how they apply to IoT device design
  • List design considerations when adding Internet capabilities for IoT devices
  • Describe design considerations that apply when designing apps for IoT devices
  • Describe smart IoT devices and how to design for them
  • Describe effective practices to design IoT devices

Exploring Virtual Reality

Introduction to Virtual Reality
  • Describe virtual reality, the importance of virtual reality, and the future of virtual reality
  • Define the types of virtual reality and when they could be used
  • Describe the beginning of virtual reality and how it has reached the point it is at today
  • Identify where virtual reality is already being used or could be used in the near future
  • Define the levels of immersion and how it affects a user in virtual reality
  • Describe the types of virtual reality hardware and their capabilities or limits
  • Compare some of the features and capabilities of current virtual reality headgear
  • Describe the tools and environments that exist for developing virtual reality games and applications
  • Distinguish and describe the differences between programming virtual reality games and applications with C++ vs. C#
  • Identify the hardware and software requirements for developing virtual reality applications with Unity
  • Describe VR, the types of VR, and the available development tools
Manipulating the VR environment
  • Describe the symptoms of virtual reality sickness and the contributing factors
  • Specify how virtual reality environments can cause nausea and other discomforts for users
  • Prevent nausea and give the user a comfortable virtual environment experience
  • Interact with a user's sense of motion and recognize how it affects virtual reality experiences
  • Describe how gaze is used and how it affects a VR environment
  • Define what a reticle is and how it can be used to help a user within a virtual reality environment
  • Work with the render scale to find a balance between sharpness and performance
  • Use head rotation and position tracking in a virtual reality environment
  • Use a touchpad, keyboard, control stick, or headgear buttons to allow users to manipulate a virtual reality environment
  • Describe how manipulating a virtual reality environment can affect a user
Creating a Virtual Reality App with Unity
  • Download and install Unity3d and associated components
  • Set up and configure a virtual reality project within Unity
  • Import and install sample files into a Unity VR project
  • Describe and use the VREyeRaycaster script to determine what the eye may be looking at
  • Use the VRInput class to determine if the user has provided any input
  • Attach the VRInteractiveItem component to GameObjects
  • Use a custom script to subscribe to and respond to VRInteractiveItem events
  • Render a reticle on objects as a user's gaze changes within the environment
  • Use Selection Radials to get confirmation an action should take place
  • Use Selection Sliders to fill in bars to get confirmation a user wants an action to take place
  • Use colliders and Rigidbody to implement realistic movement in an app
  • Use LoadSceneAsync to prevent lag or jitters when loading data in a virtual reality environment
  • Use the built-in Unity VR capabilities to build a virtual reality app
User Interfaces in Virtual Reality
  • Describe the limitations of UI interfaces in VR and the possible solutions
  • Use antialiasing and adjust the sharpness of text
  • Render 3D text within a VR environment
  • Create and use a non-diegetic Ui to display data to a user
  • Create and use a spatial UI to display data to a user
  • Create and use a Diegetic UI to display data to a user
  • Set up a blink transition that can be used in favor or player movement
  • Create a menu that can come into focus as the user targets a object or placeholder
  • Use the different types of User Interfaces to create a curved 3D menu for a VR app
  • Describe the ways in which menus can be used effectively in a VR environment
Optimizing for Unity VR
  • Describe why optimization is fundamental for a user to have a good VR experience
  • Use the Unity profiler to find areas that need optimization
  • Use the Unity Frame Debugger to investigate rendering issues and find objects that don't need to be rendered
  • Optimize the drawing of objects by removing faces of any object that will not be seen
  • Find objects that are being overdrawn and remove them to reduce wasting GPU time
  • Use occlusion culling to prevent rendering of objects that are not visible
  • Use Draw Call batching to batch draw calls to improve performance
  • Eliminate or reduce the lighting requirements to increase performance
  • Use shaders only where appropriate to increase performance
  • Use the built-in Quality Settings to balance the visual quality versus performance of the app
  • Adjust the Level of Detail (LOD) setting to reduce the number of rendered triangles as objects get farther away
  • Describe why optimization is required for VR apps and describe some of the techniques that can be used
Using GoogleVR and Unreal
  • Describe how to install Unreal Engine on a development machine
  • Configure Unreal Engine for developing Android apps
  • Create a new Blueprint-based project in Unreal Engine
  • Modify Unreal Engine project settings to facilitate GoogleVR development
  • Create a pawn that represents a user's VR viewpoint
  • Modify a Blueprint to add a line trace for gaze-based interaction
  • Implement gaze-based interaction by hit testing based on a line trace
  • Use Unreal tools to debug a line trace
  • Configure Unreal Engine settings to be able to build a VR project for Android
  • Deploy a GoogleVR app from Unreal to a connected Android device
  • Create a Steam and Unreal VR app

 

Tidsforbrug

Hele kursuskollektionen kan gennemføres på ca. 28 timer.

Form

Denne online kursuspakke består af flere forskellige kurser, som du ved tilmelding har adgang til i 365 dage. Hvert enkelt kursus er opdelt i flere kursusmoduler, som du via en oversigtsmenu kan tage i den rækkefølge, du ønsker. Modulerne indeholder lyd, billeder og tekst, der gennemgår kursusindholdet. Nogle moduler indeholder små videofilm med scenarier og cases. Ved hvert kursus har du mulighed for at teste din forståelse af indholdet med tests, som du kan tage både før, under og efter kurset. Du gennemfører kursusmodulerne via din computer eller tablet med lyd og adgang til Internettet. Du kan selv styre, hvornår du vil tage modulerne – og de kan sættes på pause undervejs. Der bliver indsat bogmærker, der hvor du er nået til, så du altid har mulighed for at fortsætte, hvor du sidst kom til.
Læs mere om vores online kurser og se svar på dine spørgsmål (FAQ)

Søgte du et andet online kursus?

Vi tilbyder over 7.000 forskellige online kurser inden for mange forskellige områder. Kontakt os på tlf. 72203000 eller kurser@teknologisk.dk, så vi kan hjælpe med at imødekomme dit behov.
Se desuden listen over vores udvalgte online kurser.

Køb online kursus til flere

Er I en afdeling, en hel virksomhed eller blot flere personer, der ønsker adgang til online kurser, så kontakt os og få et tilbud på tlf. 72203000 eller kurser@teknologisk.dk

Har du faglige spørgsmål så kontakt
Andre kigger også på