Final Exam: AI and ML for Decision-makers

WHAT YOU WILL LEARN

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  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
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  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