Python Tutorial: Unsupervised learning: basics

Want to learn more? Take the full course at https://learn.datacamp.com/courses/cluster-analysis-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi everyone! Welcome to the video of this course. In this video, we will focus on unsupervised learning, business problems that are solved using such techniques and basic plotting of points that would help us later in the course! Let's get started. While browsing through Google News, have you wondered what goes behind grouping news items together? How does the algorithm decide which articles are similar? It is the result of an unsupervised learning algorithm. It scans through the text of each article and based on frequently occurring terms, groups articles together. The group of articles shown here is based on the Indian cricket team. Through this course, you will be introduced to various clustering techniques. Similar to this example, you will also perform document clustering on text. Before we define unsupervised learning, let us try to understand the terms: labeled and unlabeled data. Imagine you have a list of points with X and Y coordinates. If only the coordinates of the points are available and there is no other characteristic available to distinguish the data points, it is called unlabeled data. At the same time, if we associate each data point with a group beforehand, say normal and danger zones, we call it labeled data. What is unsupervised learning? It is an umbrella term for a group of machine learning algorithms that are used to find patterns. The data that is used in these algorithms is not labeled, classified or characterized prior to running the algorithm. The algorithm is run, therefore, to find and explain inherent structures within the data. Common unsupervised learning algorithms are clustering, anomaly detections, and neural networks. Clustering is used to group similar data points together. Let us now move on to a specif