Python Machine Learning

Wei-Meng Lee

• Publisher: Wiley
• Year: 2019
• Print ISBN: 9781119545637
• eBook ISBN: 9781119545675
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming Algorithms
• Index: Computer Science

Python makes machine learning easy for beginners and experienced developers

With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.

Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.

• Python data science—manipulating data and data visualization
• Data cleansing
• Understanding Machine learning algorithms
• Supervised learning algorithms
• Unsupervised learning algorithms
• Deploying machine learning models

Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

CHAPTER 1
Introduction to Machine Learning

Welcome to Python Machine Learning! The fact that you are reading this book is a clear indication of your interest in this very interesting and exciting topic.

This book covers machine learning, one of the hottest programming topics in more recent years. Machine learning (ML) is a collection of algorithms and techniques used to design systems that learn from data. These systems are then able to perform predictions or deduce patterns from the supplied data.

With computing power increasing exponentially and prices decreasing simultaneously, there is no better time for machine learning. Machine learning tasks that usually require huge processing power are now possible on desktop machines. Nevertheless, machine learning is not for the faint of heart—it requires a good foundation in mathematics, statistics, as well as programming knowledge. The majority of the books in the market on machine learning go into too much detail, which often leaves beginning readers gasping for air. Most of the discussion on machine learning revolves heavily around statistical theories and algorithms, so unless you are a mathematician or a PhD candidate, you will likely find them difficult to digest. For most people, developers in particular, what they want is to have a foundational understanding of how machine learning works, and most importantly, how to apply machine learning in their applications. It is with this motive in mind that I set out to write this book.

This book will take a gentle approach to machine learning. I will attempt to do the following:

• Cover the libraries in Python that lay the foundation for machine learning, namely NumPy, Pandas, and matplotlib.
• Discuss machine learning using Python and the Scikit‐learn libraries. Where possible, I will manually implement the relevant machine learning algorithm using Python. This will allow you to understand how the various machine learning algorithms work behind the scenes. Once this is done, I will show how to use the Scikit‐learn libraries, which make it really easy to integrate machine learning into your own apps.
• Cover the common machine learning algorithms—regressions, clustering, and classifications.

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TIP

It is not the intention of this book to go into a deep discussion of machine learning algorithms. Although there are chapters that discuss some of the mathematical concepts behind the algorithms, it is my intention to make the subject easy to understand and hopefully motivate you to learn further.

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Machine learning is indeed a very complex topic. But instead of discussing the complex mathematical theories behind it, I will cover it using easy‐to‐understand examples and walk you through numerous code samples. This code‐intensive book encourages readers to try out the numerous examples in the various chapters, which are designed to be independent, compact, and easy to follow and understand.

What Is Machine Learning?

If you have ever written a program, you will be familiar with the diagram shown in Figure 1.1. You write a program, feed some data into it, and get your output. For example, you might write a program to perform some accounting tasks for your business. In this case, the data collected would include your sales records, your inventory lists, and so on. The program would then take in the data and calculate your profits or lo...

Table of contents

• Cover
• Table of Contents
• Introduction
• CHAPTER 1: Introduction to Machine Learning
• CHAPTER 2: Extending Python Using NumPy
• CHAPTER 3: Manipulating Tabular Data Using Pandas
• CHAPTER 4: Data Visualization Using matplotlib
• CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning
• CHAPTER 6: Supervised Learning—Linear Regression
• CHAPTER 7: Supervised Learning—Classification Using Logistic Regression
• CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines
• CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
• CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means
• CHAPTER 11: Using Azure Machine Learning Studio
• CHAPTER 12: Deploying Machine Learning Models
• Index
• End User License Agreement