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)
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CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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Types of Kernels

Up to this point, we only discussed one type of SVM—linear SVM. As the name implies, linear SVM uses a straight line to separate the points. In the previous section, you also learned about the use of kernel tricks to separate two sets of data that are distributed in a circular fashion and then used linear SVM to separate them.

Sometimes, not all points can be separated linearly, nor can they be separated using the kernel tricks that you observed in the previous section. For this type of data, you need to “bend” the lines to separate them. In machine learning, kernels are functions that transform your data from nonlinear spaces to linear ones (see Figure 8.17).

Illustration depicting how a kernel function transforms data from nonlinear spaces to linear ones by bending the lines to separate them.

Figure 8.17: A kernel function transforms your data from nonlinear spaces to linear ones

To understand how kernels work, let's use the Iris dataset as an example. The following code snippet loads the Iris dataset and prints out the features, target, and target names:

%matplotlib inline...