Python Tutorial : Unsupervised Learning in Python

Want to learn more? Take the full course at https://learn.datacamp.com/courses/unsupervised-learning-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Ben Wilson and I'm a Data Scientist and mathematician. We're here to learn about unsupervised learning in Python. Unsupervised learning is a class of machine learning techniques for discovering patterns in data. For instance, finding the natural "clusters" of customers based on their purchase histories, or searching for patterns and correlations among these purchases, and using these patterns to express the data in compressed form. These are examples of unsupervised learning techniques called "clustering" and "dimension reduction". Unsupervised learning is defined in opposition to supervised learning. An example of supervised learning is using the measurements of tumors to classify them as benign or cancerous. In this case, the pattern discovery is guided, or "supervised", so that the patterns are as useful as possible for predicting the label: benign or cancerous. Unsupervised learning, in contrast, is learning without labels. It is pure pattern discovery, unguided by a prediction task. You'll start by learning about clustering. But before we begin, let's introduce a dataset and fix some terminology. The iris dataset consists of the measurements of many iris plants of three different species. There are four measurements: petal length, petal width, sepal length and sepal width. These are the features of the dataset. Throughout this course, datasets like this will be written as two-dimensional numpy arrays. The columns of the array will correspond to the features. The measurements for individual plants are the samples of the dataset. These correspond to rows of the array. The samples of the iris dataset have four measurements, and so correspond to points in a four-dimensional space. This is the dimension of the dataset. We ca