Summary
In this chapter, we took a whirlwind tour through one of the most popular Python machine learning libraries: scikit-learn. We saw what kind of data this library expects. Real-world data will seldom be ready to be fed into an estimator right away. With powerful libraries, such as Numpy and, especially, Pandas, you already saw how data can be retrieved, combined, and brought into shape. Visualization libraries, such as matplotlib, help along the way to get an intuition of the datasets, problems, and solutions.
During this chapter, we looked at a canonical dataset, the Iris dataset. We also looked at it from various angles: as a problem in supervised and unsupervised learning and as an example for model verification.
In total, we have looked at four different algorithms: the Support Vector Machine, Linear Regression, K-Means clustering, and Principal Component Analysis. Each of these alone is worth exploring, and we barely scratched the surface, although we were able to implement all...
