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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What Is NumPy?

In Python, you usually use the list data type to store a collection of items. The Python list is similar to the concept of arrays in languages like Java, C#, and JavaScript. The following code snippet shows a Python list:

list1 = [1,2,3,4,5] 

Unlike arrays, a Python list does not need to contain elements of the same type. The following example is a perfectly legal list in Python:

list2 = [1,"Hello",3.14,True,5] 

While this unique feature in Python provides flexibility when handling multiple types in a list, it has its disadvantages when processing large amounts of data (as is typical in machine learning and data science projects). The key problem with Python's list data type is its efficiency. To allow a list to have non‐uniform type items, each item in the list is stored in a memory location, with the list containing an “array” of pointers to each of these locations. A Python list requires the following:

  • At least 4 bytes per pointer...