Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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What Is a Support Vector Machine?

In the previous chapter, you saw how to perform classification using logistics regression. In this chapter, you will learn another supervised machine learning algorithm that is also very popular among data scientists—Support Vector Machines (SVM). Like logistics regression, SVM is also a classification algorithm.

The main idea behind SVM is to draw a line between two or more classes in the best possible manner (see Figure 8.1).

Illustration of using support vector machines (SVM) to draw a line to separate two classes of animals based on their ear geometry and snout length.

Figure 8.1: Using SVM to separate two classes of animals

Once the line is drawn to separate the classes, you can then use it to predict future data. For example, given the snout length and ear geometry of a new unknown animal, you can now use the dividing line as a classifier to predict if the animal is a dog or a cat.

In this chapter, you will learn how SVM works and the various techniques you can use to adapt SVM for solving nonlinearly‐separable datasets.

Maximum Separability

How does SVM separate two or more...