Moore Richard - Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science

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Python Machine Learning

The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science.

By Richard Moore

Copyright 2019 - All rights reserved.

The content contained within this book may not be reproduced, duplicated or transmitted without direct written permission from the author or the publisher.

Under no circumstances will any blame or legal responsibility be held against the publisher, or author, for any damages, reparation, or monetary loss due to the information contained within this book, either directly or indirectly.

Legal Notice:

This book is copyright protected. It is only for personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part, or the content within this book, without the consent of the author or publisher.

Disclaimer Notice:

Please note the information contained within this document is for educational and entertainment purposes only. All effort has been executed to present accurate, up to date, reliable, complete information. No warranties of any kind are declared or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content within this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.

By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, that are incurred as a result of the use of information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.

Table of Contents

Introduction

Machine learning is a fairly new domain, however it quickly became the latest trend on which every IT specialist is trying to jump, and rightly so. This technical field can be used to solve a wide variety of todays problems. The purpose of Python Machine Learning is to guide you step by step through all the basic concepts of machine learning.

Throughout the course of this book you will learn about all the specialised tools you will need on your journey. In addition, you will learn a variety of machine learning algorithms and techniques by combining theory with practice. You will be applying everything you learn on real datasets and learn the practical use of every concept you study.

Keep in mind that in order to benefit the most from using this ultimate guide for beginners, you will have to go through every practical application and dissect it until you understand it. In addition, you will have to start practicing on your own in order to build a proper foundation. You dont have to be a computer science major to understand machine learning, however you do need a great deal of determination and most importantly, practice!

Chapter 1: Why Machine Learning?

So youre interested in IT, programming, or all that is tech and recently you keep hearing about machine learning. Wherever you turn your head to explore something new, this concept keeps coming up. At this point you probably expect some shady guy wearing a trench coat to approach you in a dark alley and ask you, Hey kid, interested in some machine learning? You probably thought that there must be something worth studying here if everyone who knows how to write a few lines in Python keeps talking about it. So what is machine learning and why is everyone so crazy about it?

In this chapter we will address these questions and more. Its important to understand why machine learning is such a hot topic and why many see it as the future in all that is technology. You already took the first step in exploring this field, but if you get a better idea about the application of machine learning, you will definitely become more motivated to study it and learn it.

The Importance of Machine Learning

Machine learning started out as a component of Artificial Intelligence, and therefore its definition is somewhat tied to it. In essence, machine learning software learns from experience, almost like humans. Keep in mind that in this case experience refers to data and the ability to learn does not involve direct programming. When a machine learning application is exposed to data it will absorb it, learn from it, adapt itself based on it and evolve. In other words, systems that are powered by machine learning doesn't need to be instructed by someone how to search for something and where to find it. Instead, it relies on its own algorithms to learn from the data and then apply what it learned in order to perform better and repeat the cycle. See? Its almost human, but no this is not from the realm of science fiction. Machine learning has been here for quite some time and its becoming more and more of an industry standard in research as well as product design.

In fact, the idea of machine learning is at least as old as the Enigma Machine, however its ability to perform a massive number of fully automated mathematical operations became possible only in the past couple of decades due to the massive leaps in computer processing power. Furthermore, a number of new technologies were developed in recent years and they are the ones that take advantage of machine learning the most.

To get a better idea why machine learning is so important, you need to know where its actually applied. Are you aware of Googles fully automated, self-driving car? You have machine learning to thank for that. Do you use Facebook, Amazon, or Netflix? Check, check and check, they all rely on machine learning to power their ability to recommend you things you may be interested in. Thats how they know you love funny cat videos. Computer systems can learn all of this by running all the data you generate through a filter, keep only what is relevant to you and what fits your pattern, and then give you the accurate results you dont even know youre looking for.

Machine learning seems to have evolved so suddenly, even though it had been theorized by the great mathematicians who lived centuries ago (yes, even before the invention of the computer), only because of market demand and its real world application for mass consumption. For instance, you may have also heard about machine learning in the same sentence as Big Data. Thats because it is highly sophisticated nowadays and it allows us to process and analyse massive amounts of data that no human could handle otherwise. Furthermore, machine learning has led to a revolution in the way we extract and analyze data because it allows us to automate the algorithms and therefore replace the slow, cumbersome statistical methods that used to be the norm.

Applied Machine Learning

Machine learning has been lately integrated in pretty much everything that is tech and you will often not even notice it or think of it. Earlier we mentioned the more obvious sectors where machine learning algorithms take over. However, you will also encounter them in anything that has to do with web search results, real time online ads, email spam protection, network protection, image recognition software, and much more. All of these products or services have one thing in common. They all take advantage of machine learning because of the massive amounts of data that need to be processed and analyzed.

Data analysis used to be quite tricky before the implementation of machine learning algorithms. It involved a trial and error approach, and it mightve worked two decades ago. However, today data is produced at such a staggering speed that it is impossible to keep up with its exponential growth. The more the computer processing power advances, the more tech we devise, and the more data and information we generate. For instance, even your smart home devices generate huge quantities of data that is collected for analysis in order to improve those devices further. Machine learning is the only alternative to the traditional techniques. It provides us with fast and powerful algorithms that produce accurate results. Machine learning will not go anywhere anytime soon, quite the contrary.

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