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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Linear Regression

In machine learning, linear regression is one of the simplest algorithms that you can apply to a dataset to model the relationships between features and labels. In Chapter 5, we started by exploring simple linear regression, where we could explain the relationship between a feature and a label by using a straight line. In the following section, you will learn about a variant of simple linear regression, called multiple linear regression, by predicting house prices based on multiple features.

Using the Boston Dataset

For this example, we will use the Boston dataset, which contains data about the housing and price data in the Boston area. This dataset was taken from the StatLib library, which is maintained at Carnegie Mellon University. It is commonly used in machine learning, and it is a good candidate to learn about regression problems. The Boston dataset is available from a number of sources, but it is now available directly from the sklearn.datasets package. This...