Final Exam: AI and ML for Decision-makers
- 1 video | 32s
- Includes Assessment
- Earns a Badge
Final Exam: AI and ML for Decision-makers will test your knowledge and application of the topics presented throughout the AI and ML for Decision-makers journey.
WHAT YOU WILL LEARN
Summarize the concept and use cases for clustering, identify three types of clustering: hierarchical, density-based, centroiddefine the concept and identify use cases for classification. identify common classifiersdefine the concept and identify use cases for machine learningdefine strategies for evaluating the accuracy of classification modeldefine the concept and identify use cases for machine learning, differentiate between supervised and unsupervised machine learningidentify use cases for machine learningdefine the concept and identify use cases for text miningdefine the concept and identify use cases for graph analysisdefine the concept and identify use cases for anomaly detectiondefine the concept and identify use cases for neural networkssummarize the concept and use cases for aidetermine the types of data are used with aiidentify ai tools and technologiesdescribe the ai lifecycle and its elementsdevelop specific, measurable, and objective questions for your organizationdetermine how to get the right data for your ai projectsummarize the concept, components, and purpose of the data analytics maturity modelidentify and analyze the uses of data analyticsidentify the emerging trends in data analytics and their role across industriesrecognize the purpose and uses of data cleaning tools. identify several data cleaning toolsrecognize the purpose and uses of data analysis tools. identify main data analysis toolsrecognize the purpose and uses of data visualization tools. identify several data visualization toolsdistinguish between centralized, decentralized and hybrid data team structures. identify benefits and challenges of each type of structure. identify use cases for ai and how ai can be used in your industryidentify the responsibilities of data analystsidentify the responsibilities of machine learning engineersevaluate their organization's data-driven cultureoutline the ethical concepts managers should think about when adopting ai/ mlidentify key principles of data ethicsdefine the concept of data ethics and determine responsibilities of leaders and managersdefine the concept and types of data bias. identify strategies to recognize and avoid data bias
identify and interpret bar chartsidentify and interpret pie chartsidentify and interpret scatterplot, map, histogram, bubble chartidentify common visualization tools. select the visualization tools for your data teamidentify and apply best practices for designing compelling visualsuse size and grouping items to design effective visualsusing color to design effective visualsrecognize and address common visualization mistakes (use of color, using wrong charts)recognize truncated graphs, exaggerated scaling, ignored conventionsrecognize the visualizations with numbers that don’t add up and 3d distortionssummarize the concept and purpose of data storytellingidentify and refine an insight for a data storyidentify and analyze the audience for a data storyidentify the role of cloud computing in ai and the need for cloud computing in ai. identify use casesidentify benefits and challenges of cloud computing. identify security concernsidentify steps for implementing a cloud ai strategylocate and identify the elements (back end and front end) of cloud computing architectureintroduce saas and ai as a service. describe uses and importance of ai as a servicesummarize the role of ai tools in data management and governancedefine the concepts and explore use cases for data ops, mlops, model ops, aiops and devsecopsdefine version control and its uses. discuss the importance of version control in mldiscuss version control tools and their uses (dvc, mlmd, modeldb, paychyderm)define mlops and explore uses cases for mlops. identify elements of mlops infrastructure. define the need and elements of production model governancedefine dataops and identify its usesidentify the elements of dataops pipelinesummarize the concept of ml pipelines, use cases and preparing mlops pipelineidentify the characteristics of automated ml pipelinesummarize the development environment, staging and moving to productionanalyze the importance of ci/cd in mlsummarize ml testing tools and frameworks
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