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.
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