Smith - Python Machine Learning: The Crash Course for Beginners to Programming and Deep Learning, Artificial Intelligence, Neural Networks and Data Science. Scikit Learn, Tensorflow, Pandas and Numpy.

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Python

Machine Learning

The Crash Course for Beginners to Programming and Deep Learning, Artificial Intelligence, Neural Networks and Data Science.

Scikit Learn, Tensorflow, Pandas and Numpy.

Copyright 2019 by Django Smith - All rights reserved.

The following Book is reproduced below with the goal of providing information that is as accurate and reliable as possible. Regardless, purchasing this Book can be seen as consent to the fact that both the publisher and the author of this book are in no way experts on the topics discussed within and that any recommendations or suggestions that are made herein are for entertainment purposes only. Professionals should be consulted as needed prior to undertaking any of the action endorsed herein.

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Introduction

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

The following chapters will discuss everything that you need to know in order to get started with Python machine learning. Machine learning is a growing field, one that is taking over the world of technology and helping us to do and learn things like never before. And when you add in some of the cool things that you are able to do with the Python language along with it, you will find that this kind of learning is easy to work with as well.

The beginning of this guidebook is going to spend some time looking at machine learning and what it all entails. We will look at the basics of machine learning as well as some of the most common types of machine learning including supervised and unsupervised machine learning. Once we have a good idea of how we can work with machine learning, it is time to move on to a bit of work with Python. We will explore how to set up our Python environment to work with machine learning, before moving on to some different things that you can do with Python machine learning including data pre-proccing, linear regression, and more!

Once we have the ideas of machine learning and Python all setup, it is time to learn a lot of the other things that you will be able to do when it comes to machine learning. We will look at how to work with decision trees and random forests, support vector regressions, Nave Bayes problems and KNN classification problems. To end this guidebook, we will look at data wrangling visualization, web applications, and accelerated data analysis that can be done with the help of the Python language.

There are so many things that you are able to do when it comes to machine learning, and this is a field that is going to grow and grow over time. Make sure to check out this guidebook to help you get started learning how to work with Python machine learning!

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!

Chapter 1: Understanding Machine Learning

You will find that the Python language is going to be able to help us out a lot when it comes to working with machine learning. But before we really get into all of this, we need to take a look at machine learning and some of the different parts that come with it. The field of machine learning is growing like crazy, and being able to use it in the proper manner, and understand all of the parts that go along with it can make a world of difference.

Machine learning is going to be a part of the data science world, a part that is going to deal with any technology that can be trained how to learn based on the input that it is given. With a traditional program on the computer, the program is going to be trained to only do what is in the code. It is never going to look at the input that the user has, and it is not able to make decisions on its own. The program is going to just repeat what is in the code.

When we take a look at machine learning though, things are going to be a bit different. With this, you will find that the program is going to be able to learn. There are several ways that it is able to do this including looking at the patterns that it can find in the data, trial, and error, and even learning from all of the data or the input that it gets from the user.

The idea that is going to be found with machine learning is that it will ensure that the program is able to learn how to read data, and then from that information, it is going to be able to make some of its own decisions. There are many times when you want to have the programmer to be able to react based on how the user is interacting with it, without a programmer having to guess all of the options that are going to be presented there.

For example, when we are working with speech recognition, we can see how this process would be almost impossible if we used traditional coding methods. But we can use some of the features that come with machine learning in order to teach the code to learn how to understand the other person is talking.

The first definition of machine learning was coined in 1959 by Arthur Samuel. He defined machine learning in this manner Field of study that enables computers to learn without being explicitly programmed.

This is one of the neat things about machine learning. The machine is able to figure out patterns out of a large amount of data, even if the programmer didnt specifically tell it how to behave. This can be helpful in uses such as speech recognition, search engines, and for companies who need to search through large amounts of data to find patterns and make decisions about how to act in the future.

How does machine learning work?

With that definition of machine learning in place, the next thing that we need to take a look at is how we can get started with this process. Before we look at all of the details that come with machine learning, it is important to first look at the way that the human brain is going to work, and then we can compare that to machine learning to get a better outlook on how all of this should work.

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