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 matplotlib?

As the adage goes, “A picture is worth a thousand words.” This is probably most true in the world of machine learning. No matter how large or how small your dataset, it is often very useful (and many times, essential) that you are able to visualize the data and see the relationships between the various features within it. For example, given a dataset containing a group of students with their family details (such as examination results, family income, educational background of parents, and so forth), you might want to establish a relationship between the students' results with their family income. The best way to do this would be to plot a chart displaying the related data. Once the chart is plotted, you can then use it to draw your own conclusions and determine whether the results have a positive relationship to family income.

In Python, one of the most commonly used tools for plotting is matplotlib. Matplotlib is a Python 2D plotting library that...