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

Reshaping Arrays

You can reshape an array to another dimension using the reshape() function. Using the b5 (which is a rank 1 array) example, you can reshape it to a rank 2 array as follows:

b5 = b5.reshape(1,-1)
print(b5)
'''
[[9 8 7 6 5]]
''' 

In this example, you call the reshape() function with two arguments. The first 1 indicates that you want to convert it into rank 2 array with 1 row, and the ‐1 indicates that you will leave it to the reshape() function to create the correct number of columns. Of course, in this example, it is clear that after reshaping there will be five columns, so you can call the reshape() function as reshape(1,5). In more complex cases, however, it is always convenient to be able to use ‐1 to let the function decide on the number of rows or columns to create.

Here is another example of how to reshape b4 (which is a rank 2 array) to rank 1:

b4.reshape(-1,)
'''
[9 8 7 6 5]
''' 

The ‐...