Final Exam: AI Practitioner
Artificial intelligence | Intermediate
- 1 Video | 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 workflowscompare AI Practitioner to ML Engineer and list fundamental differences in their workflowsidentify key benefits of AI Optimization and specify improvements which can be achieved from AI Optimizationcompare AI Practitioner to AI Engineer and list fundamental differences in their workflowsdescribe the principle of Gradient Descent Optimization in AI and specify cases in which Gradient Descent Optimization is usedspecify the types of AI Optimization and describe key differences in the approachesdescribe the principle of AdaGrad Optimization in AI and specify cases in which AdaGrad Optimization is usedspecify how to tune hyperparameters using Grid Search approachwork with Keras to create and train a feed-forward neural network model and demonstrate its performancedescribe how to load and use external data with Microsoft CNTKlist model types present in Amazon ML and specify their purposesspecify how to tune hyperparameters using the Bayesian methoddescribe the principle of Stochastic Gradient Descent Optimization in AI and specify cases in which SGD is usedwork with Python to apply pre-processing techniques to housing price data and troubleshoot CNTK machine learning regression model creation and training using this datadescribe the main features of intelligent systems and define the concept of IISdefine Epochs and Batch size in CNTK and specify how to choose optimal values for best performancedescribe the role of hyper parameters in deep learning neural network modelsdescribe the capabilities of Amazon ML regarding feature processingwork with Python libraries to design an environment for Markov Decision Process for Self-Driving technologyname primary components of intelligent information systems and their purposespecify how Spark ML pipeline can be used for creating and tuning ML modelslist possible challenges and common problems when developing IISname the features of Spark Data Frame and list useful operations for working with Spark Data Framesdefine core and convolutional layers specifying their role in the overall neural networkcreate training data using Spark toolkit and develop Spark Estimator in Pythondescribe how to create more complex AI models using Keras functional APIwork with Python libraries to build high-level components of Markov Decision Process for Self-Driving technologycompare and contrast the use of Amazon ML and Google Cloud Platformdescribe the process of hyperparameter tuning and name multiple approaches to the processspecify cases in which it is advantageous to use Amazon ML over other platforms
identify possible data sources for working with Amazon MLdescribe Keras Sequential model API and specify how it is used for developing AIdescribe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online predictiondescribe the role of AI Practitioner in a company and identify key responsibilitieslist possible applications of intelligent information systemsspecify multiple approaches to how data can be split using Amazon MLspecify the skillset needed to become an AI Practitioner and name commonly used toolslist possible operations with Resilient Distributed Datasets and specify their rolework with CNTK evaluation tools to evaluate previously created CNTK machine learning modelidentify how CNTK can be used for Model Visualizationdescribe the principle of Momentum Optimization in AI and specify cases in which Momentum Optimization is useddescribe the principle of Adam Optimization in AI and specify cases in which Adam Optimization is useddescribe the role of hyperparameters in common machine learning models and approacheswork with CNTK to create and train a feed-forward neural network as well as demonstrate its performancedefine pooling and recurrent layers specifying their role in the overall neural networklist possible sources of data for a Spark Data Frame and describe how to import these into Sparkspecify the role of AI practitioner when developing AI products and relationship with other developersspecify cases in which it is advantageous to use SPARK over other platformscompare and contrast the use of Amazon ML and Azure MLrecognize why IIS development is a growing field and specify demand for IIS developmentspecify multiple techniques and approaches to pre-processing provided by Kerasdescribe how to create a Resilient Distributed Datasetdescribe how to create a Spark Data Framecompare AI Practitioner to Data Scientist/AI Scientist and list fundamental differences in their workflowscompare and contrast Keras with MS CNTKname multiple libraries which allow for hyperparameter tuning and describe how to use these librariesdescribe how real-time prediction is made in Amazon MLspecify cases in which it is advantageous to use Keras over other platformsdescribe the role of hyperparameters in AI Development and specify their importancespecify cases in which it is advantageous to use CNTK over other platforms
IN THIS COURSE
1.AI Practitioner33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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