Prompt Engineering for Statistics and Machine Learning E-Learning

Master the art of Prompt Engineering for Statistics and Machine Learning with our comprehensive training. Learn to craft effective prompts for fundamental statistical and machine learning techniques using platforms like ChatGPT and Bard. Compare outputs to enhance your skills in statistical analysis and machine learning applications. Cover descriptive statistics, hypothesis testing, and advanced topics like regression analysis, decision trees, and k-means clustering. Book now to optimize your data analysis and machine learning capabilities!

This Learning Kit with more than 5 hours of learning is divided into three tracks:

Course content

Computing Descriptive Statistics Using Prompt Engineering

• Statistics is a branch of mathematics that involves the collection, analysis, interpretation, presentation, and organization of data. It provides a framework for making inferences and drawing generalizable conclusions from observed information and it offers great tools to uncover patterns, trends, and relationships within datasets. Begin this course by exploring two important types of statistics – descriptive and inferential statistics. Next, learn how to compute and interpret descriptive statistics in code, including measures of central tendency and dispersion, mean and median, and range. Then use generative artificial intelligence (AI) tools to help interpret visualizations and understand the nuance between the different statistical measures and when you would choose to use them. After completing this course, you will have a solid understanding of how to calculate, interpret, and visualize descriptive statistics using Python and be able to leverage prompt engineering to help implementation and interpretation.

Running Statistical Tests Using Generative AI Tools

• Hypothesis testing is an important part of inferential statistics that involves assessing sample data to draw conclusions about a population parameter. Begin this course by exploring how hypothesis tests work, the results they generate, and how you interpret those results. You will learn how you set up the null and alternative hypotheses for tests and how to interpret the results which includes the test statistic and the p-value. Then you will discover the different types of t-tests, such as one-sample, two-sample, and paired samples. Finally, you will investigate the use of generative artificial intelligence (AI) tools to implement one-sample t-tests and interpret the results. At course completion, you will have a solid understanding of the basics of hypothesis testing and how prompt engineering can help you implement and interpret these statistical tests.

Prompt Engineering for Hypothesis Testing

• T-tests and analysis of variance (ANOVA) are statistical methods used to compare means between groups and assess whether observed differences are statistically significant. In this course, you will perform two-sample t-tests, comparing two independent groups to determine if the difference between their means is statistically significant. You will use ChatGPT and Google Bard to help ensure that your samples meet the assumptions of the t-test. Then you will visualize and interpret the characteristics of your data and run the right variation of the t-test based on your data. Next, you will run a paired sample t-test with help from generative artificial intelligence (AI) tools. Finally, you will use ANOVA to compare multiple samples simultaneously, use prompt engineering to determine when to use ANOVA, and use post-hoc analysis after running ANOVA to identify which groups or categories are significantly different. After completing this course, you will have a solid understanding of t-tests and ANOVA and be able to leverage Generative AI tools to help you with your analysis.

Prompt Engineering for Machine Learning

• Machine learning involves creating models that dynamically change based on the data from which they are created. Within machine learning, three fundamental problems—regression, classification, and clustering—are the focus of a variety of solution techniques. Begin this course by conducting regression analysis. You will analyze and visualize data to get a sense of the variables with predictive power, split data into training and test sets, and train a model. Then you will interpret the R-squared metric to evaluate how well the regression model has performed. Next, you will create a classification model for predicting categorical targets and split your data into test and training data to train a logistic regression model. You will also explore the impact of training a model on imbalanced data, and with generative artificial intelligence (AI) assistance, see how you can mitigate this by leveraging oversampling and undersampling techniques. Finally, you will perform clustering, train a k-means clustering model, and evaluate it using the silhouette and Davies-Bouldin scores. At course completion, you will have a good understanding of key concepts of machine learning and how to perform regression analysis, classification of data, and clustering.

Assessment:

• Final Exam: Prompt Engineering Use Cases