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    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
    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
    Ebook121 pages1 hour

    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

    By Steven Cooper

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    About this ebook

    If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading.


    Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. 

    ★★ Grab your copy today and learn ★★

    ♦ The different types of learning algorithm that you can expect to encounter

    ♦ The numerous applications of machine learning

    ♦ The different types of machine learning and how they differ

    ♦ The best practices for picking up machine learning

    ♦ What languages and libraries to work with

    ♦ The future of machine learning

    ♦ The various problems that you can solve with machine learning algorithms

    ♦ And much more...


    Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.
     

    Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!

    LanguageEnglish
    PublisherSteven Cooper
    Release dateSep 7, 2018
    ISBN9781386291855
    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
    Author

    Steven Cooper

    Steven Cooper is a freelance writer, video producer, and the author of four previous novels. A former television reporter, he has received multiple Emmy awards and nominations, a National Edward R. Murrow Award, and Associated Press awards. He taught writing at Rollins College (Winter Park, FL) from 2007 to 2012.

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      Book preview

      Machine Learning for Beginners - 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

      Steven Cooper

      C:\Users\Roland\Eigene Dateien Roland\Arbeit\Arbeit Amazon\Bücher\Data Science from Scratch\Data Science Logo\Data Science_Logo_Cover.png

      Table of Contents

      Preface

      Introduction

      Chapter 1: Machine Learning - What and Why?

      Chapter 2: Machine Learning vs. Artificial Intelligence and Deep Learning

      Reflection Questions

      Chapter 3: Machine Learning Workflows

      Reflection Questions

      Chapter 4: About Computer Learning (Supervised, Unsupervised, Reinforcement)

      Reflection Questions

      Chapter 5: Supervised Learning

      Parametric/Non-Parametric Algorithms

      Linear Regression

      Parametric Problems

      Logistic Regression

      Non-Parametric Algorithms - K-Nearest Neighbors

      Decision Trees

      Support Vector Machines

      Kernels

      Reflection Questions

      Chapter 6: Unsupervised Learning

      Clustering

      Dimensionality Reduction

      Neural Networks

      Deep Learning

      Reflection Questions

      Chapter 7: Reinforcement Learning

      Reflection Questions

      Chapter 8: Best Practices in Machine Learning

      Focus on One Discipline at a Time

      Don’t Reinvent the Wheel

      Work with Other Projects

      Reflection Questions

      Chapter 9: Learning Algorithms and Applications

      Learning Algorithms

      Text-based Services

      Computer Vision

      Audio Processing

      Database Mining

      Reflection Questions

      Chapter 10: Examples of Successful Machine Learning

      Spotify and Pandora

      PayPal

      Uber

      Facebook

      Gmail

      Closing

      Reflection Questions

      Chapter 11: Future of Machine Learning

      Finance

      Language

      Medical Industry

      Self-Driving Cars

      Robots and Androids

      Closing

      Reflection Questions

      Conclusion

      About the Author

      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.

      Legal & Disclaimer

      The following document is reproduced below with the goal of providing information that is as accurate and reliable as possible.

      This declaration is deemed fair and valid by both the American Bar Association and the Committee of Publishers Association and is legally binding throughout the United States.

      Furthermore, the transmission, duplication or reproduction of any of the following work including specific information will be considered an illegal act irrespective of if it is done electronically or in print. This extends to creating a secondary or tertiary copy of the work or a recorded copy and is only allowed with an express written consent from the Publisher. All additional right reserved.

      The information in the following pages is broadly considered to be a truthful and accurate account of facts, and as such any inattention, use or misuse of the information in question by the reader will render any resulting actions solely under their purview. There are no scenarios in which the publisher or the original author of this work can be in any fashion deemed liable for any hardship or damages that may befall them after undertaking information described herein.

      Additionally, the information in the following pages is intended only for informational purposes and should thus be thought of as universal. As befitting its nature, it is presented without assurance regarding its prolonged validity or interim quality. Trademarks that are mentioned are done without written consent and can in no way be considered an endorsement from the trademark holder.

      Preface

      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.

      It’s 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

      There is absolutely no question about it: artificial intelligence is the future. However, artificial intelligence is also the present. It’s one of the faster-growing tech fields and, as I’m sure you’re aware, the future is only going to see more and more demand for capable artificial intelligence programmers.

      I’m not certain why you’re reading this book. Perhaps you’re already on the path to studying artificial intelligence and machine learning in college or a university, and you’re 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 you’re wanting to switch fields and take advantage of the massive wave of demand that’s hitting for artificial intelligence, data analysis, and machine learning as we speak. Or perhaps you’re just a hobbyist interested in learning exactly what this machine learning that everybody’s talking about is.

      Regardless of your ultimate machine, you’re 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, it’s going to start breaking the concept of machine learning down into bite-sized chunks. We’re 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. We’ll 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 you’re finished with all the finer details and the algorithms, you’ll be able to go into a deeper level on any of the topics in this book and start to have an idea of how

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