This section—focusing on ChatGPT for data scientists—covers data preprocessing, exploratory data analysis (EDA), regular expressions, and building a recommendation engine before delving into AI ethics and detecting data biases with ChatGPT.

In this theoretical section we’ll discuss topics such as imbalanced data, Naïve Bayes algorithms what a confusion matrix is, and define metrics such as precision, recall and F1 score. All these will be the foundation for our Python case study involving classifying user reviews based on text data, also known as sentiment analysis.

In the final section of this course, we’ll delve into sentiment analysis by classifying user reviews. We’ll use our own 365 data and train a Naïve Bayes algorithm to classify user reviews as good or bad. At the end of the section we’ll test our model on a new validation set.