More Applied Data Science with Python

In “Applied Information Extraction in Python,” 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’ll 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’ll 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’ll explore Transformer models such as ChatGPT and Large Language Models to extract information from large datasets. This is the final course in “More Applied Data Science with Python,” 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.

In “Applied Unsupervised Learning in Python,” you will learn how to use algorithms to find interesting structure in datasets. You will practice applying, interpreting, and refining unsupervised machine learning models to solve a diverse set of problems on real-world datasets. This course will show you how to explore unlabelled data using several techniques: dimensionality reduction and manifold learning for condensing and visualizing high-dimensional data, clustering to reveal interesting groups and outliers, topic modeling for summarizing important themes in text, methods for dealing with missing data, and more. This course also covers best practices associated with different techniques, as well as demonstrating how unsupervised learning can be used to improve supervised prediction. This is the second course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.

In “Data Mining in Python,” you will learn how to extract useful knowledge from large-scale datasets. This course introduces basic concepts and general tasks for data mining. You will explore a wide range of real-world data sets, including grocery store, restaurant reviews, business operations, social media posts, and more. You will learn how to formally describe real-world information with general data representations (e.g., itemsets, vectors, matrices, sequences, and more). You will then learn how to formulate data in the wild with one or more of these representations. This course will teach you how to characterize and explain your data by looking for patterns and similarities, which are basic building blocks for advanced analysis and machine learning models. This is the first course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.