Steven Cooper - Machine Learning for Beginners: An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work

Machine Learning

for Beginners

An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work

Steven Cooper

Copyright 2018 Steven Cooper

All rights reserved.

No part of this guide may be reproduced in any form without permission in writing from the publisher except in the case of review.

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The main goal of this book is to help people take the best actionable steps possible towards a career in data science. The need for data scientists is growing exponentially as the internet, and online services continue to expand.

Book Objectives

This book will help you:

• Know more about the fundamental principles of machine learning and what you need to become a skilled data scientist.

• Have an elementary grasp of machine learning concepts and tools that will make this work easier to do.

• Have achieved a technical background in machine learning and appreciate its power.

Target Users

The book is designed for a variety of target audiences. The most suitable users would include:

• Newbies in computer science techniques

• Professionals in software applications development and social sciences

• Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way

• Students and academicians, especially those focusing on machine learning and software development

Is this book for me?

This book is for those who are interested in machine learning. There are a lot of skills that a data scientist needs, such as coding, intellectual mindset, eagerness to make new discoveries, and much more.

Its important that you are interested in this because you are obsessed with this kind of work. Your driving force should not be money. If it is, then this book is not for you.

Introduction

T here is absolutely no question about it: artificial intelligence is the future. However, artificial intelligence is also the present. Its one of the faster-growing tech fields and, as Im sure youre aware, the future is only going to see more and more demand for capable artificial intelligence programmers.

Im not certain why youre reading this book. Perhaps youre already on the path to studying artificial intelligence and machine learning in college or a university, and youre wanting a book that will put you on an excellent path forward and help you figure out the context and rationale behind your lessons as you push on. Perhaps youre wanting to switch fields and take advantage of the massive wave of demand thats hitting for artificial intelligence, data analysis, and machine learning as we speak. Or perhaps youre just a hobbyist interested in learning exactly what this machine learning that everybodys talking about is.

Regardless of your ultimate machine, youre reading the right book. This book is intended to break down machine learning and the many, many concepts which build it up.

The book will begin by looking at machine learning and what it is, as well as why one would benefit from looking into machine learning and learning the nuances of this specific area of artificial intelligence. This will give you a clear sense of purpose as you go through the rest of the book and start thinking of ways to apply this in a day-to-day sense.

Afterward, its going to start breaking the concept of machine learning down into bite-sized chunks. Were going to start with the biggest and most nebulous concepts, and then slowly work our way down to things at the smallest and most intricate levels. Well be studying the numerous different paradigms for machine learning and how they can be programmed and implemented within your own code, as well as getting a feel for the algorithms which build them up. Throughout all of this, the goal is simply to foster an appreciation for the immense and difficult topic that is machine learning.

The goal at this point is to build a very intimate knowledge of the inner workings of machine learning so that by the time youre finished with all the finer details and the algorithms, youll be able to go into a deeper level on any of the topics in this book and start to have an idea of how they all work at a more strenuous and taxing level. As such, were going to be spending a lot of time at the algorithmic level and breaking every type of machine learning down into its various disciplines, as well as discussing the primary terms used within the context of machine learning and what they mean.

After all of that, were going to start looking at the applications of machine learning. This will help you to get a much better feel for machine learning and how it works, which can be a great boon for your understanding of how these many concepts that were covering can be reasonably applied. This will run the gamut from discussions of anti-spam measures implemented by email providers, to the use of machine learning algorithms in applications like the development of smarter robotics.