Clustering—an unsupervised machine learning approach used to group data based on similarity—is used for work in network analysis, market segmentation, search results grouping, medical imaging, and anomaly detection. K-means clustering is one of the most popular and easy to use clustering algorithms. In this course, Fred Nwanganga gives you an introductory look at k-means clustering—how it works, what it’s good for, when you should use it, how to choose the right number of clusters, its strengths and weaknesses, and more. Fred provides hands-on guidance on how to collect, explore, and transform data in preparation for segmenting data using k-means clustering, and gives a step-by-step guide on how to build such a model in Python.

Generators are a concept unique to Python. They're incredibly helpful if you know how and when to use them. Simply put, generators are possibly the best way to iterate through large and complex data sets. In this course, Megan Amendola covers the basics you need to know about Python generators, starting from what they are, how to create them, what they’re for, and ways to use them. Megan teaches you the benefit of using Python generators to improve your program’s performance and save memory when working with large data sets using generator functions. She also covers what Python’s yield keyword is and what it does, how to create a generator expression, how to combine multiple generators into a pipeline, and more. Megan also provides challenges and solutions as you go along so you can test your knowledge as you learn.