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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Getting Started with Scikit‐learn

The easiest way to get started with machine learning with Scikit‐learn is to start with linear regression. Linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables). For example, imagine that you have a set of data comprising the heights (in meters) of a group of people and their corresponding weights (in kg):

%matplotlib inline
import matplotlib.pyplot as plt
# represents the heights of a group of people in meters
heights = [[1.6], [1.65], [1.7], [1.73], [1.8]]
# represents the weights of a group of people in kgs
weights = [[60], [65], [72.3], [75], [80]]
plt.title('Weights plotted against heights')
plt.xlabel('Heights in meters')
plt.ylabel('Weights in kilograms')
plt.plot(heights, weights, 'k.')
# axis range for x and y
plt.axis([1.5, 1.85, 50, 90])

When you...