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In 鈥淎pplied Information Extraction in Python,鈥� you will learn how to extract useful information from free-text data, which is a type of string data created when people type. Examples of free-text data include names of people or organizations, location information such as cities and zip codes, or other elements like stock prices or clinical diagnoses. Free-text data is found everywhere, from magazine articles to social media posts, and can be complex to analyze. In this course, you鈥檒l use applied machine learning and text-mining techniques to analyze free-text data. You will learn how to identify named entities and tag them with appropriate types of classifications, using real-world data from business, politics, and healthcare. You鈥檒l develop multiple approaches to recognize and extract named entities and attributes of interest from free-text data, ranging from regular expressions to neural network models. Finally, you鈥檒l explore Transformer models such as ChatGPT and Large Language Models to extract information from large datasets. This is the final course in 鈥淢ore Applied Data Science with Python,鈥� a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the following courses from the Applied Data Science with Python Specialization: Introduction to Data Science in Python, Applied Machine Learning in Python, and Applied Text Mining in Python.