Final Exam: AI Practitioner

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

WHAT YOU WILL LEARN

  • compare AI Practitioner to AI Developer and list fundamental differences in their workflows
    compare AI Practitioner to AI Engineer and list fundamental differences in their workflows
    compare AI Practitioner to Data Scientist/AI Scientist and list fundamental differences in their workflows
    compare AI Practitioner to ML Engineer and list fundamental differences in their workflows
    compare and contrast Keras with MS CNTK
    compare and contrast the use of Amazon ML and Azure ML
    compare and contrast the use of Amazon ML and Google Cloud Platform
    create training data using Spark toolkit and develop Spark Estimator in Python
    define core and convolutional layers specifying their role in the overall neural network
    define Epochs and Batch size in CNTK and specify how to choose optimal values for best performance
    define pooling and recurrent layers specifying their role in the overall neural network
    describe how real-time prediction is made in Amazon ML
    describe how to create a Resilient Distributed Dataset
    describe how to create a Spark Data Frame
    describe how to create more complex AI models using Keras functional API
    describe how to load and use external data with Microsoft CNTK
    describe Keras Sequential model API and specify how it is used for developing AI
    describe the capabilities of Amazon ML regarding feature processing
    describe the main features of intelligent systems and define the concept of IIS
    describe the principle of AdaGrad Optimization in AI and specify cases in which AdaGrad Optimization is used
    describe the principle of Adam Optimization in AI and specify cases in which Adam Optimization is used
    describe the principle of Gradient Descent Optimization in AI and specify cases in which Gradient Descent Optimization is used
    describe the principle of Momentum Optimization in AI and specify cases in which Momentum Optimization is used
    describe the principle of Stochastic Gradient Descent Optimization in AI and specify cases in which SGD is used
    describe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online prediction
    describe the process of hyperparameter tuning and name multiple approaches to the process
    describe the role of AI Practitioner in a company and identify key responsibilities
    describe the role of hyperparameters in AI Development and specify their importance
    describe the role of hyperparameters in common machine learning models and approaches
    describe the role of hyper parameters in deep learning neural network models
  • identify how CNTK can be used for Model Visualization
    identify key benefits of AI Optimization and specify improvements which can be achieved from AI Optimization
    identify possible data sources for working with Amazon ML
    list model types present in Amazon ML and specify their purposes
    list possible applications of intelligent information systems
    list possible challenges and common problems when developing IIS
    list possible operations with Resilient Distributed Datasets and specify their role
    list possible sources of data for a Spark Data Frame and describe how to import these into Spark
    name multiple libraries which allow for hyperparameter tuning and describe how to use these libraries
    name primary components of intelligent information systems and their purpose
    name the features of Spark Data Frame and list useful operations for working with Spark Data Frames
    recognize why IIS development is a growing field and specify demand for IIS development
    specify cases in which it is advantageous to use Amazon ML over other platforms
    specify cases in which it is advantageous to use CNTK over other platforms
    specify cases in which it is advantageous to use Keras over other platforms
    specify cases in which it is advantageous to use SPARK over other platforms
    specify how Spark ML pipeline can be used for creating and tuning ML models
    specify how to tune hyperparameters using Grid Search approach
    specify how to tune hyperparameters using the Bayesian method
    specify multiple approaches to how data can be split using Amazon ML
    specify multiple techniques and approaches to pre-processing provided by Keras
    specify the role of AI practitioner when developing AI products and relationship with other developers
    specify the skillset needed to become an AI Practitioner and name commonly used tools
    specify the types of AI Optimization and describe key differences in the approaches
    work with CNTK evaluation tools to evaluate previously created CNTK machine learning model
    work with CNTK to create and train a feed-forward neural network as well as demonstrate its performance
    work with Keras to create and train a feed-forward neural network model and demonstrate its performance
    work with Python libraries to build high-level components of Markov Decision Process for Self-Driving technology
    work with Python libraries to design an environment for Markov Decision Process for Self-Driving technology
    work with Python to apply pre-processing techniques to housing price data and troubleshoot CNTK machine learning regression model creation and training using this data

IN THIS COURSE

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    AI Practitioner
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