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)
End User License Agreement


In this chapter, you witnessed the use of Pandas to represent tabular data. You learned about the two main Pandas data structures: Series and DataFrame. I attempted to keep things simple and to show you some of the most common operations that you would perform on these data structures. As extracting rows and columns from DataFrames is so common, I have summarized some of these operations in Table 3.1.

Table 3.1: Common DataFrame Operations

Extract a range of rows using row numbers df[2:4]
Extract a single row using row number df.iloc[2]
Extract a range of rows and range of columns df.iloc[2:4, 1:4]
Extract a range of rows and specific columns using positional values df.iloc[2:4, [1,3]]
Extract specific row(s) and column(s) df.iloc[[2,4], [1,3]]
Extract a range of rows using labels df['20190601':'20190603']
Extract a single row based on its label df.loc['20190601']