Final Exam: AI and ML for Decision-makers

Intermediate
  • 1 video | 32s
  • Includes Assessment
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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, centroid
    define the concept and identify use cases for classification. identify common classifiers
    define the concept and identify use cases for machine learning
    define strategies for evaluating the accuracy of classification model
    define the concept and identify use cases for machine learning, differentiate between supervised and unsupervised machine learning
    identify use cases for machine learning
    define the concept and identify use cases for text mining
    define the concept and identify use cases for graph analysis
    define the concept and identify use cases for anomaly detection
    define the concept and identify use cases for neural networks
    summarize the concept and use cases for ai
    determine the types of data are used with ai
    identify ai tools and technologies
    describe the ai lifecycle and its elements
    develop specific, measurable, and objective questions for your organization
    determine how to get the right data for your ai project
    summarize the concept, components, and purpose of the data analytics maturity model
    identify and analyze the uses of data analytics
    identify the emerging trends in data analytics and their role across industries
    recognize the purpose and uses of data cleaning tools. identify several data cleaning tools
    recognize the purpose and uses of data analysis tools. identify main data analysis tools
    recognize the purpose and uses of data visualization tools. identify several data visualization tools
    distinguish 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 industry
    identify the responsibilities of data analysts
    identify the responsibilities of machine learning engineers
    evaluate their organization's data-driven culture
    outline the ethical concepts managers should think about when adopting ai/ ml
    identify key principles of data ethics
    define the concept of data ethics and determine responsibilities of leaders and managers
    define the concept and types of data bias. identify strategies to recognize and avoid data bias
    identify and interpret bar charts
    identify and interpret pie charts
    identify and interpret scatterplot, map, histogram, bubble chart
    identify common visualization tools. select the visualization tools for your data team
    identify and apply best practices for designing compelling visuals
    use size and grouping items to design effective visuals
    using color to design effective visuals
    recognize and address common visualization mistakes (use of color, using wrong charts)
    recognize truncated graphs, exaggerated scaling, ignored conventions
    recognize the visualizations with numbers that don’t add up and 3d distortions
    summarize the concept and purpose of data storytelling
    identify and refine an insight for a data story
    identify and analyze the audience for a data story
    identify the role of cloud computing in ai and the need for cloud computing in ai. identify use cases
    identify benefits and challenges of cloud computing. identify security concerns
  • identify steps for implementing a cloud ai strategy
    locate and identify the elements (back end and front end) of cloud computing architecture
    introduce saas and ai as a service. describe uses and importance of ai as a service
    summarize the role of ai tools in data management and governance
    define the concepts and explore use cases for data ops, mlops, model ops, aiops and devsecops
    define version control and its uses. discuss the importance of version control in ml
    discuss 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 governance
    define dataops and identify its uses
    identify the elements of dataops pipeline
    summarize the concept of ml pipelines, use cases and preparing mlops pipeline
    identify the characteristics of automated ml pipeline
    summarize the development environment, staging and moving to production
    analyze the importance of ci/cd in ml
    summarize ml testing tools and frameworks
    describe the role of nlp in text analysis and language understanding and identify nlp use cases
    identify common evaluation metrics, including accuracy, precision, recall, and f1-score
    understand the role of data privacy regulations and compliance in ai initiatives
    define data bias and its potential impact on ai outcomes and decision-making
    describe the role of transparency and fairness in ai model development
    illustrate the potential impact of ai on job roles and workforce dynamics
    outline the managerial responsibilities in communicating ai strategies to stakeholders
    discuss the role of transparency and fairness in ai model development
    outline the process of feature engineering and its impact on model performance
    recognize emerging trends in ai/ml evaluation, such as explainable ai and fairness auditing
    evaluate methods for defining clear success metrics for ai projects
    discuss the importance of data governance frameworks and their components
    describe the managerial responsibilities in communicating ai strategies to stakeholders
    review the potential impacts of ai on business models and revenue streams
    identify emerging trends in ai/ml evaluation, such as explainable ai and fairness auditing
    evaluate the concept of cross-validation and its role in estimating model performance
    define common evaluation metrics, including accuracy, precision, recall, and f1-score
    explore methods for auditing ai models for fairness and inclusivity
    illustrate the role of data privacy regulations and compliance in ai initiatives
    analyze methods for defining clear success metrics for ai projects
    describe the concept of data lineage and its significance in ai solutions
    evaluate the potential impacts of ai on business models and revenue streams
    outline the benefits and challenges associated with integrating ai and ml into business approaches
    recall emerging trends in ai/ml evaluation, such as explainable ai and fairness auditing
    identify the concept of data lineage and its significance in ai solutions
    recall the importance of data governance frameworks and their components
    analyze the trade-offs between model complexity and interpretability
    describe the role of managers in driving ai adoption and change management
    describe data bias and its potential impact on ai outcomes and decision-making
    identify the role of transparency and fairness in ai model development

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