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

Summary

In this final chapter, you saw how to deploy your machine learning model using the Flask micro‐framework. You also saw how you can view the correlations between the various features and then only use those most useful features for training your model. It is always useful to evaluate several machine learning algorithms and choose the best performing one so that you can choose the correct algorithm for your specific dataset.

I hope that this book has given you a good overview of machine learning, and that it has jumpstarted and inspired you to continue learning. As I have mentioned, this book is a gentle introduction to machine learning, and there are some details that were purposely omitted to make it easy to follow along. Nevertheless, if you have tried all of the exercises in each chapter, you should now have a pretty good understanding of the fundamentals of machine learning!