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
1
Cover
2
Introduction
11
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
15
Index
16
End User License Agreement

Creating NumPy Arrays

Before using NumPy, you first need to import the NumPy package (you may use its conventional alias np if you prefer):

import numpy as np 

The first way to make NumPy arrays is to create them intrinsically, using the functions built right into NumPy. First, you can use the arange() function to create an evenly spaced array with a given interval:

a1 = np.arange(10)        # creates a range from 0 to 9
print(a1)                 # [0 1 2 3 4 5 6 7 8 9]
print(a1.shape)           # (10,) 

The preceding statement creates a rank 1 array (one‐dimensional) of ten elements. To get the shape of the array, use the shape property. Think of a1 as a 10×1 matrix.

You can also specify a step in the arange() function. The following code snippet inserts a step value of 2:

a2 = np.arange(0,10,2)    # creates a range from 0 to 9, step 2
print(a2)                 # [0 2 4 6 8] 

To create an array of a specific size filled with 0s, use the zeros() function:

a3 = np.zeros(5...