Final Exam: Prompt Engineering Use Cases

Intermediate
  • 1 video | 32s
  • Includes Assessment
  • Earns a Badge
Final Exam: Prompt Engineering Use Cases will test your knowledge and application of the topics presented throughout the Prompt Engineering Use Cases journey.

WHAT YOU WILL LEARN

  • Use prompt engineering to learn machine learning and statistics
    configure and explore generative ai chatbots
    use prompt engineering to learn statistics and machine learning
    compute median and mean in python using prompt engineering
    compute mean and median in python using prompt engineering
    describe the benefits of calculating range and inter-quartile range
    calculate range and inter-quartile range
    calculate variance and standard deviation
    interpret skewness and kurtosis using ai chatbots
    analyze images with generative ai
    interpret images with generative ai
    set up and explore generative ai chatbots
    recall how p-values work
    understand hypothesis testing
    analyze p-values using alpha levels
    introduce t-tests
    identify assumptions of t-tests
    use prompt engineering help to test data for normality with
    perform the one-sample t-test
    use the one-sample t-test end-to-end
    perform the one-sample t-test end-to-end
    test data for normality with prompt engineering help
    identify the two-sided and one-sided t-tests
    explore the two-sided and one-sided t-tests
    explore assumptions of t-tests
    interpret p-values using alpha levels
    introduce the paired-samples t-test
    use the welch's t-test
    use prompt engineering to interpret results of the two sample t-test
    run the welch's t-test
  • identify the appropriate test to test data for equal variances
    describe how to test data for normality
    test data for equal variances
    test data for normality
    describe the two-sample t-tests
    introduce the two-sample t-tests
    run the paired-samples t-test
    test the assumptions of the paired samples t-test
    describe anova
    use anova and tukey hsd tests
    test assumptions of anova
    run anova and tukey hsd tests
    introduce anova
    train a clustering model
    balance imbalanced data
    balance out imbalanced data
    train a model on an imbalanced dataset
    train a classification model on an imbalanced dataset
    split data and then train a classification model
    split data and train a classification model
    analyze the performance of clustering
    evaluate the performance of clustering
    evaluate the performance of classification models
    review data for classification
    describe machine learning models
    understand regression models
    analyze data for classification
    analyze relationships in data
    interpret relationships in data
    introduce machine learning models

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