Final Exam: AI Practitioner
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 workflowscompare AI Practitioner to AI Engineer and list fundamental differences in their workflowscompare AI Practitioner to Data Scientist/AI Scientist and list fundamental differences in their workflowscompare AI Practitioner to ML Engineer and list fundamental differences in their workflowscompare and contrast Keras with MS CNTKcompare and contrast the use of Amazon ML and Azure MLcompare and contrast the use of Amazon ML and Google Cloud Platformcreate training data using Spark toolkit and develop Spark Estimator in Pythondefine core and convolutional layers specifying their role in the overall neural networkdefine Epochs and Batch size in CNTK and specify how to choose optimal values for best performancedefine pooling and recurrent layers specifying their role in the overall neural networkdescribe how real-time prediction is made in Amazon MLdescribe how to create a Resilient Distributed Datasetdescribe how to create a Spark Data Framedescribe how to create more complex AI models using Keras functional APIdescribe how to load and use external data with Microsoft CNTKdescribe Keras Sequential model API and specify how it is used for developing AIdescribe the capabilities of Amazon ML regarding feature processingdescribe the main features of intelligent systems and define the concept of IISdescribe the principle of AdaGrad Optimization in AI and specify cases in which AdaGrad Optimization is useddescribe the principle of Adam Optimization in AI and specify cases in which Adam Optimization is useddescribe the principle of Gradient Descent Optimization in AI and specify cases in which Gradient Descent Optimization is useddescribe the principle of Momentum Optimization in AI and specify cases in which Momentum Optimization is useddescribe the principle of Stochastic Gradient Descent Optimization in AI and specify cases in which SGD is useddescribe the process of batch prediction in Amazon ML and identify cases in which batch prediction is more desirable than online predictiondescribe the process of hyperparameter tuning and name multiple approaches to the processdescribe the role of AI Practitioner in a company and identify key responsibilitiesdescribe the role of hyperparameters in AI Development and specify their importancedescribe the role of hyperparameters in common machine learning models and approachesdescribe the role of hyper parameters in deep learning neural network models
identify how CNTK can be used for Model Visualizationidentify key benefits of AI Optimization and specify improvements which can be achieved from AI Optimizationidentify possible data sources for working with Amazon MLlist model types present in Amazon ML and specify their purposeslist possible applications of intelligent information systemslist possible challenges and common problems when developing IISlist possible operations with Resilient Distributed Datasets and specify their rolelist possible sources of data for a Spark Data Frame and describe how to import these into Sparkname multiple libraries which allow for hyperparameter tuning and describe how to use these librariesname primary components of intelligent information systems and their purposename the features of Spark Data Frame and list useful operations for working with Spark Data Framesrecognize why IIS development is a growing field and specify demand for IIS developmentspecify cases in which it is advantageous to use Amazon ML over other platformsspecify cases in which it is advantageous to use CNTK over other platformsspecify cases in which it is advantageous to use Keras over other platformsspecify cases in which it is advantageous to use SPARK over other platformsspecify how Spark ML pipeline can be used for creating and tuning ML modelsspecify how to tune hyperparameters using Grid Search approachspecify how to tune hyperparameters using the Bayesian methodspecify multiple approaches to how data can be split using Amazon MLspecify multiple techniques and approaches to pre-processing provided by Kerasspecify the role of AI practitioner when developing AI products and relationship with other developersspecify the skillset needed to become an AI Practitioner and name commonly used toolsspecify the types of AI Optimization and describe key differences in the approacheswork with CNTK evaluation tools to evaluate previously created CNTK machine learning modelwork with CNTK to create and train a feed-forward neural network as well as demonstrate its performancework with Keras to create and train a feed-forward neural network model and demonstrate its performancework with Python libraries to build high-level components of Markov Decision Process for Self-Driving technologywork with Python libraries to design an environment for Markov Decision Process for Self-Driving technologywork 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
1.AI Practitioner33sUP NEXT
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