Denne kurspakken dekker de viktigste aspektene ved kunstig intelligens (AI), maskinlæring og virtuell virkelighet (VR). Du vil blant annet lære å gjenkjenne ulike former for kunstig intelligens, teknikker for å bygge kunstig intelligenssystemer, samt feilsøking av forskjellige problemer som kunstig intelligens og Machine Learning-prosesser kan støte på. I tillegg vil du få en introduksjon til Virtual Reality, hvordan du designer Virtual Reality-apper i Unity, GoogleVR og Unreal, samt hvilke problemer du kan støte på når du utvikler applikasjoner.
| Kurs som inngår i biblioteket | Varighet |
| Introduction to Virtual Reality | 0,70 |
| Manipulating the VR Environment | 0,67 |
| Creating a Virtual Reality App with Unity | 1,03 |
| User Interfaces in Virtual Reality | 0,75 |
| Optimizing for Unity VR | 0,67 |
| Android Cardboard and Unity VR | 0,60 |
| Using GoogleVR and Unreal | 0,75 |
| Planning AI Implementation | 0,80 |
| TensorFlow: Introduction to Machine Learning | 1,68 |
| TensorFlow: Simple Regression and Classification Models | 1,63 |
| TensorFlow: Deep Neural Networks and Image Classification | 1,30 |
| Tensorflow: Sentiment Analysis with Recurrent Neural Networks | 1,00 |
| Tensorflow: K-means Clustering with TensorFlow | 1,00 |
| Tensorflow: Building Autoencoders in TensorFlow | 0,80 |
| Tensorflow: Word Embeddings & Recurrent Neural Networks | 0,70 |
| TensorFlow: Convolutional Neural Networks for Image Classification | 1,40 |
| Understanding Bots: Chatbot Architecture | 0,95 |
| Understanding Bots: Building Bots with Dialogflow | 0,95 |
| Understanding Bots: Chatbot Advanced Concepts and Features | 1,42 |
| Understanding Bots: Amazon Alexa Skills Development | 1,10 |
| Introduction to Artificial Intelligence | 0,85 |
| Search Problems | 0,73 |
| Constraint Satisfaction Problems | 0,50 |
| Adversarial Problems | 0,70 |
| Uncertainty | 0,78 |
| Machine Learning | 0,80 |
| Reinforcement Learning | 0,58 |
| Introducing Natural Language Processing | 0,70 |
| Developing AI and ML Solutions with Java: AI Fundamentals | 1,10 |
| Developing AI and ML Solutions with Java: Machine Learning Implementation | 1,50 |
| Developing AI and ML Solutions with Java: Neural Network and Neuroph Framework | 1,80 |
| Developing AI and ML Solutions with Java: Neural Network and NLP Implementation | 0,90 |
| Developing AI and ML Solutions with Java: Expert Systems and Reinforcement Learning | 0,80 |
| Machine & Deep Learning Algorithms: Introduction | 0,77 |
| Machine & Deep Learning Algorithms: Regression & Clustering | 0,82 |
| Machine & Deep Learning Algorithms: Data Preperation in Pandas ML | 1,07 |
| Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML | 1,40 |
| Data Tools: Technology Landscape & Tools for Data Management | 0,45 |
| Data Tools: Machine Learning & Deep Learning in the Cloud | 0,38 |
| Architecting Balance: Designing Hybrid Cloud Solutions | 0,95 |
| Architecting Balance: Hybrid Cloud Implementation with AWS & Azure | 1,13 |
| Applied Deep Learning: Unsupervised Data | 1,47 |
| Applied Deep Learning: Generative Adversarial Networks and Q-Learning | 0,75 |
| Convo Nets for Visual Recognition: Filters & Feature Mapping in CNN | 1,12 |
| Convo Nets for Visual Recognition: Computer Vision & CNN Architectures | 0,82 |
| Applied Predictive Modeling | 1,13 |
| Advanced Reinforcement Learning: Principles | 1,22 |
| Advanced Reinforcement Learning: Implementation | 1,58 |
| Building Neural Networks: Development Principles | 1,35 |
| Building Neural Networks: Artificial Neural Networks Using Frameworks | 1,92 |
| Enterprise Architecture: Architectural Principles & Patterns | 1,58 |
| Enterprise Architecture: Design Architecture for Machine Learning Applications | 1,00 |
| Building ML Training Sets: Introduction | 1,10 |
| Building ML Training Sets: Preprocessing Datasets for Linear Regression | 0,90 |
| Bayesian Methods: Bayesian Concepts & Core Components | 1,02 |
| Bayesian Methods: Implementing Bayesian Model and Computation with PyMC | 0,80 |
| Bayesian Methods: Advanced Bayesian Computation Model | 0,87 |
| Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools | 1,00 |
| Implementing Deep Learning: Optimized Deep Learning Applications | 0,72 |
| Build & Train RNNs: Neural Network Components | 0,62 |
| Build & Train RNNs: Implementing Recurrent Neural Networks | 0,82 |
| Automation Design & Robotics | 0,60 |
| Deep Learning with Keras | 1,93 |
| Model Management: Building Machine Learning Models & Pipelines | 0,53 |
| Model Management: Building & Deploying Machine Learning Models in Production | 0,93 |
| Getting Started with Neural Networks: Biological & Artificial Neural Networks | 0,98 |
| Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms | 0,75 |
| Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling | 0,60 |
| Fundamentals of Sequence Model: Language Model & Modeling Algorithms | 0,33 |
| ConvNets: Introduction to Convolutional Neural Networks | 1,02 |
| ConvNets: Working with Convolutional Neural Networks | 0,72 |
| Improving Neural Networks: Neural Network Performance Management | 1,95 |
| Improving Neural Networks: Loss Function & Optimization | 1,07 |
| Improving Neural Networks: Data Scaling & Regularization | 1,63 |
| Linear Algebra and Probability: Fundamentals of Linear Algebra | 1,68 |
| Linear Algebra & Probability: Advanced Linear Algebra | 1,73 |
| Linear Regression Models: Introduction to Linear Regression | 1,30 |
| Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras | 0,70 |
| Linear Regression Models: Multiple and Parsimonious Linear Regression | 1,18 |
| Linear Regression Models: An Introduction to Logistic Regression | 0,97 |
| Linear Regression Models: Simplifying Regression and Classification with Estimators | 0,60 |
| ML/DL Best Practices: Machine Learning Workflow Best Practices | 0,88 |
| ML/DL Best Practices: Building Pipelines with Applied Rules | 1,07 |
| Enterprise Services: Enterprise Machine Learning with AWS | 1,23 |
| Enterprise Services: Machine Learning Implementation on Microsoft Azure | 1,22 |
| Enterprise Services: Machine Learning Implementation on Google Cloud Platform | 1,03 |
| Math for Data Science & Machine Learning | 1,03 |
| ML Algorithms: Multivariate Calculation & Algorithms | 0,65 |
| ML Algorithms: Machine Learning Implementation Using Calculus & Probability | 0,52 |
| NLP for ML with Python: NLP Using Python & NLTK | 1,05 |
| NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn | 0,68 |
| Convolutional Neural Networks: Fundamentals | 0,77 |
| Convolutional Neural Networks: Implementing & Training | 0,52 |
| Predictive Modelling Best Practices: Applying Predictive Analytics | 1,50 |
| Reinforcement Learning: Essentials | 0,50 |
| Reinforcement Learning: Tools & Frameworks | 0,58 |
| Refactoring ML/DL Algorithms: Techniques & Principles | 1,10 |
| Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms | 0,98 |
| Research Topics in ML and DL | 0,70 |
| Training Neural Networks: Implementing the Learning Process | 1,67 |
| Training Neural Networks: Advanced Learning Algorithms | 1,68 |
| Introduction to Machine Learning and Supervised Learning | 0,90 |
| Supervised Learning Models | 0,70 |
| Unsupervised Learning | 0,60 |
| Neural Networks | 0,70 |
| Convolutional and Recurent Neural Networks | 0,70 |
| Applying Machine Learning | 0,65 |
| AI and ML Solutions with Python: Machine Learning and Data Analytics | 1,10 |
| AI and ML Solutions with Python: Supervised, Unsupervised and Deep Learning | 1,50 |
| AI and ML Solutions with Python: Deep Learning and Neural Network Implementation | 1,00 |
| AI and ML Solutions with Python: Implementing ML Algorithm Using scikit-learn | 1,20 |
| AI and ML Solutions with Python: Implementing Robotic Process Automation | 1,00 |
