Anderson - Hands On Machine Learning with Python: Concepts and Applications for Beginners

HANDS ON MACHINE LEARNING WITH PYTHON

Concepts and Applications

John Anderson & Peter Morgan

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Table of Contents

Copyright 2018 by AI Sciences

All rights reserved.

First Printing, 2018

Edited by Davies Company

Ebook Converted and Cover by Pixels Studio

Publised by AI Sciences LLC

ISBN-13: 978-1724731968

ISBN-10: 1724731963

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Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.

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To my Wife Chelsea

Authors Biography

John Anderson is a data science researcher and expert in machine learning. After finishing his undergraduate degree in computer science at 2007, he went on to do data analysis in California. He's now an active data scientist researcher in social media, finance, and statistical computing applications.

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Preface

Machine learning will automate jobs that most people thought could only be done by people.

Dave Waters

Why Read This Book

Machine learning is now changing how business, science, and engineering are done. Today machine learning might have already affected you. If you used a search engine or looked for products machine learning could be there in the background that made the results come out faster or personalized.

Machine learning has been directly or indirectly transforming our lives. Many experts even say that were just scratching the surface. We havent yet taken full advantage of what machine learning can bring.

So if you want to take advantage of machine learning and recognize the opportunities, its best to have a working knowledge about it. Aside from learning the field and positioning yourself to become more valuable in your organization (or start a business focused in machine learning), studying machine learning can become an amazing experience for you. Its not easy and it will take a lot of time. Even if you read this book 5 times, it wont be enough to really explore the potential and current capabilities of machine learning. This is just the start of your journey.

Who This Book is For

Well, first you should now be at least comfortable with programming and Python. Well give a crash course here and discuss the most important things about Python and programming. And you can always do a Google search whenever youre stuck or unfamiliar with something (Stack Overflow and Stack Exchange are really good resources and communities).

Many other readers picked up this book because they want to shift to a new career (data scientist and machine learning engineer are the hottest jobs right now and the pays just incredible). Aside from awesome financial incentives (including a very cool tech office), machine learning engineers have the chance of working with the brightest minds from different fields. In fact, some who have PhDs in physics and statistics applied their expertise in tackling machine learning challenges.

There are also other readers who are just curious about machine learning. What is it and does it have the potential to wipe out humans? We cant give a definitive answer to that because even experts are clueless of what the potential opportunity and threat machine learning can really bring. If you want to figure it out yourself and satisfy your curiosity, read on and hopefully search for other resources so you can learn more.

Overview of what youll learn

Our goal here is to show you examples and insights on how to do machine learning (step by step as promised) so in the near future, you can initiate and handle projects on your own. More importantly, well focus on how to think properly about machine learning (gaining the understanding). Tools and techniques come and go but if you have a solid foundation, youll always be in a position to take advantage of this yet developing technology.

To start, well download and install the necessary tools to get you going. Here well be using mostly Python, Anaconda, Jupyter Notebook, and TensorFlow. Next is youll get a Python crash course so you can quickly review the most important concepts and dive straight to machine learning next.

To motivate you, well quickly explore a simple (and popular) example of the use of machine learning (Titanic Survival Prediction). Its a general example that has wide applications and implications. Once you understand how to predict survival rates, youll be half-ready to tackle many machine learning projects.

It was just half of the battle. You should then get a solid foundation about machine learning (Supervised Learning, Unsupervised Learning, Deep Learning). Well explore several examples so you can really see the potential and current applications of machine learning. Then also in the succeeding chapters well discuss each one of those in detail.

Near the end well discuss how to improve our machine learning model. Well try to improve the accuracy and performance of our model so it can be more useful for predictions, optimizations, and other applications.