CODING - Machine Learning with Python: A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python. Neural Networks and Data Science from Pre-Processing to Deep Learning

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Machine Learning with Python

A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python, Neural Networks, and Data Science from Pre-Processing to Deep Learning

Mark Coding

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. This book 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, and 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, which 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

C ongratulations on purchasing Machine Learning for Python and thank you for doing so.

The following chapters will discuss everything that we need to know in order to get started with machine learning for Python. There are a lot of different parts that come with this topic, and being able to work through it, and learn as much as possible to make it happen with your data analysis. Many companies want to go through with data analysis, but you will find that many times they are not certain about how to go through this process and make it work for their needs.

In this guidebook, we are going to run through the different steps that are needed to work with machine learning, Python, and data science. To start this guidebook, we are going to take a look at some of the basics for artificial intelligence and machine learning. These basics can help us to take a look at how we are going to be able to handle some of the algorithms and more that we are able to focus on with data science later on. And then we will move on to some of the basics that are needed to make sure that we are able to install Python on our computer system and get machine learning to work with Python to see the best results.

From here, we are going to take a closer look at some of the different parts that we need to know in order to handle machine learning and Python working together. We will look at what a data analysis is all about, the steps that go with this, how machine learning and Python will work together, We will also take some time to explore some of the basics of deep learning, and the other parts that we need to bring into play to learn the step by step process of making our data analysis work the way that we want.

Working with machine learning for Python can be a great way to help us to really see some results. When you are ready to learn more about the Python coding language, make sure to check out this guidebook to help you get started on the right track.

There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible, please enjoy it!

Chapter 1: Artificial Intelligence
and Machine Learning

B efore we are able to get into some of the different topics that are found in this guidebook, we need to first get a good look at a number of topics that are going to help us out with this. Namely, the idea of artificial intelligence and machine learning. There are many individuals and businesses who dont understand how these two are similar or different, and that can get us into a lot of trouble if we arent able to sort them outright from the beginning.

That is why we are going to take our time here and look at how each one works and what we are able to do with them. You will find that basically, artificial intelligence is an umbrella term for a lot of different types of computer science work that you are able to do, and machine learning is a type of artificial intelligence. This can help us to see why there are several similarities that fall with them and can get us off to a good start of working with these topics .

What is Artificial Intelligence?

The first thing that we are going to take a look at here is the basics of artificial intelligence. This is going to form some of the foundations of what we will see when it comes to machine learning and more that we are going to do later on. So having a solid understanding of what this topic is all about, and how we are able to use it for our data science projects can be important.

Artificial intelligence is a useful practice because it is going to make it possible for the machines and systems that we use to learn from experience, adjust to new inputs, and perform tasks almost in the same manner that a human would. Most of the examples that we are going to hear about artificial intelligence today, from self-driving cars to computers who are able to play chess, are going to rely pretty hard on deep learning, as well as something that is known as natural language processing. With the help of these technologies, a computer can be trained in order to accomplish a specific task, simply by going through and processing a large amount of data, and then finding the patterns that are found in that data.

The term artificial intelligence was coined in 1956, but in the last few years, artificial intelligence has become more popular. This is because there is an increase in the volume of data, advanced types of algorithms that we are able to use, and improvements in computing power and storage.

Early research into this topic in the 50s was going to explore a lot of topics like symbolic methods and problem-solving. Then in the 60s, the US Department of Defense took some interest in this topic and spent their time looking at ways they could train a computer to mimic the reasoning that we find in humans. For example, a project is known as DARPA, or the Defense Advanced Research Projects Agency completed projects on street mapping in the 70s. This project was able to help produce personal intelligent assistants in 2003, which was a long time before Cortana, Siri, and Alexa were the big names that we know today.

Some of this early work helped to pave the way for automation and formal reasoning that we are going to see in a lot of computers today, including systems for decision support, and systems of smart searching, that we are able to be designed to augment and complement the abilities of a human. There are a lot of benefits that come with this kind of process and learning how to make it happen, and how you are able to use this for your own needs in business as well.

There are a lot of reasons why this artificial intelligence is going to be so important. AI is able to help automate some of the repetitive learning and the discovery that you can do with data. This is going to be different than what we will see with hardware-driven and robotic automation. Instead of going through and automating some of the manual tasks that we usually handle, this process is going to perform frequent, high-volume, and computerized tasks without fatigue and in a reliable manner. For this kind of automation, an inquiry by a human is going to be essential to ensure that we can set up the system while asking the right questions the whole time.

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