Final Exam: AI Apprentice

Artificial intelligence    |    Intermediate
  • 1 Video | 30m 32s
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Final Exam: AI Apprentice will test your knowledge and application of the topics presented throughout the AI Apprentice track of the Skillsoft Aspire AI Apprentice to AI Architect Journey.


  • classify different types of Convolutional Neural Networks by their structure and purpose
    compare artificial superintelligence with artificial general intelligence and specify the multiple factors needed to achieve them
    compare cognitive modeling and artificial intelligence
    compare image processing to Computer Vision
    compare image processing to traditional methods of solving image problems
    compare multiple approaches to AI development to distinguish key differences between them
    compare the major differences between intelligent systems including search algorithms, machine learning systems, probabilistic models, neural networks, and reinforcement learning systems
    compare the performance and functionality of Python AI toolbox to R AI toolbox
    configure the Python environment for developing AI
    define general intelligence in terms of AI tools known today and recognize the amount of work needed to achieve any AGI
    define Human-Computer Interaction as a multidisciplinary field essential to computer science and describe its importance for the success of software companies
    define hybrid learning and describe examples of its use
    define narrow artificial intelligence, describe multiple areas of its use in the modern world, and recognize the latest research
    define reactive and limited memory systems and describe reactive AI, limited memory AI, and a combination of both
    define symbolic learning and describe examples of its use
    describe and distinguish between different types of modeling tools
    describe and distinguish between multiple Python AI libraries
    describe basic concepts in Computer Vision
    describe distinguishing features of adaptive, interactive, iterative, and contextual cognitive models
    describe factors that make Python one of the most popular programming languages
    describe how a CV is used in electronics and why cheap consumer electronics are not possible without CV
    describe how a CV is used in the aerospace industry and list the responsibilities of a CV system on an aircraft
    describe how a CV is used in the automotive industry and its role in the development of self-driving cars
    describe how the success of AI solutions in narrow fields is a combination of adequate task, good data, and appropriate tools and list fields that are most impacted by AI
    describe the principles of prototyping and distinguish between a prototype and a demonstration product
    describe the principles of the anthropomorphic approach to HCI
    describe the Python programming language and recognize its role in AI development
    describe the role Computer Vision plays in the industry and associated trends
    describe the role of a user-oriented approach in the success of AI applications
    describe the steps needed to create deep learning models and identify guidelines for using them
  • describe the steps needed to create machine learning models and identify guidelines for using them
    describe true research on self-aware AI and compare it with common views on the future of AI
    describe why big data improve AI performance and accuracy by specifying how collecting large amounts of data creates opportunities for new AI development and research
    describe why using artificial intelligence is becoming important today and list multiple factors that make the use of AI in business necessary for competitive advantage
    differentiate between interpreted and compiled programming languages
    distinguish between an intelligent system and pre-programmed logic using several definitions of artificial intelligence and specify the scope of AI applications
    identify and describe problems that can be solved using Computer Vision
    identify different types of cognitive models and name popular cognitive modeling applications
    identify reasons why the iterative approach has shown to be most practical when designing software applications
    identify the advantages of using Python when developing AI
    identify the main steps in the HCI process and name multiple methodologies used
    illustrate how AI can be part of a Computer Vision solution
    illustrate how computer science is connected with cognitive modeling
    list the components involved in human-computer interaction (HCI) studies and specify their role
    list the steps needed to create an object detection neural network and describe how object detection is performed
    list the tools commonly used for HCI studies and specify their purpose
    name and describe basic concepts in and cognition and cognitive modeling
    name and describe different types of cognitive learning
    recognize how CI/CD became essential to any kind of software company and list multiple factors that make CI/CD important for AI companies
    recognize how the performance of Convolutional Neural Network revolutionized CV
    recognize major AI tools used in the industry
    recognize the most recent research breakthroughs in AI and how they might be used, and list applications of AI that are already on the market
    recognize the multidisciplinary nature of HCI and list the areas most involved in the studies
    specify how AI has affected cognitive modeling and enhanced its power
    specify multiple disciplines involved in cognitive modeling and describe their role
    specify the advantages of Jupyter Notebooks and create Jupyter Notebook files connected to the appropriate kernel environment
    specify the advantages of the Google Collab environment and create files in the environment
    specify the role of Anaconda in keeping clear working environments
    specify why explainability research in AI is required for developing user-friendly applications
    troubleshoot usability of an AI application prototype


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    AI Apprentice


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