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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Types of Linear Regression

In the previous chapter, you learned how to get started with machine learning using simple linear regression, first using Python, and then followed by using the Scikit‐learn library. In this chapter, we will look into linear regression in more detail and discuss another variant of linear regression known as polynomial regression.

To recap, Figure 6.1 shows the Iris dataset used in Chapter 5, “Getting Started with Scikit‐learn for Machine Learning.” The first four columns are known as the features, or also commonly referred to as the independent variables. The last column is known as the label, or commonly called the dependent variable (or dependent variables if there is more than one label).

“Illustration presenting the Iris dataset in which the first 4 columns are called as the features, or independent variables and the last column is known as the label, or the dependent variable.”

Figure 6.1: Some terminologies for features and label

In simple linear regression, we talked about the linear relationship between...