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
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Deploying ML

The main goal of machine learning is to create a model that you can use for making predictions. Over the past few chapters in this book, you learned about the various algorithms used to build an ideal machine learning model. At the end of the entire process, what you really want is to make your model accessible to users so that they can utilize it to do useful tasks, like making predictions (such as helping doctors with their diagnosis, and so forth).

A good way to deploy your machine learning model is to build a REST (REpresentational State Transfer) API, so that the model is accessible by others who may not be familiar with how machine learning works. Using REST, you can build multi‐platform front‐end applications (such as iOS, Android, Windows, and so forth) and pass the data to the model for processing. The result can then be returned back to the application. Figure 12.1 summarizes the architecture that we will use for deploying our machine learning model...