In this chapter, we will make use of one of the first algorithmically described machine learning algorithms for classification, the perceptron and adaptive linear neurons. We will start by implementing a perceptron step by step in Python and training it to classify different flower species in the Iris dataset. This will help us to understand the concept of machine learning algorithms for classification and how they can be efficiently implemented in Python. Discussing the basics of optimization using adaptive linear neurons will then lay the groundwork for using more powerful classifiers via the scikit-learn machine-learning library in Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn.
The topics that we will cover in this chapter are as follows:
Building an intuition for machine learning algorithms
Using pandas, NumPy, and matplotlib to read in, process, and visualize data
Implementing linear classification...