Machine learning—the ability of a machine to give correct answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You'll explore Jupyter notebooks, a technology widely used in academic and commercial circles with support for running inline code.

With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.

By the end of this course, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!