Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners to Understand Data Science Through the Business Applications

168 Pages·2020·4.323 MB·English

by  Hack, Samuel

MACHINE LEARNING
2 Books in 1:
An Introduction Math Guide for Beginners to
Understand Data Science Through the
Business Applications
Samuel Hack
Copyright © 2020 by Samuel Hack - All rights reserved
The book is only for personal use. No part of this publication may be reproduced, distributed, or
transmitted in any form or by any means, including photocopying, recording, or other electronic or
mechanical methods, without the prior written permission of the publisher, except in the case of brief
quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright
law.
TABLE OF CONTENTS
YOUR FREE GIFT
MACHINE LEARNING FOR BEGINNERS
INTRODUCTION
THE PURPOSE OF THIS BOOK
WHAT IS ARTIFICIAL INTELLIGENCE?
HOW IS MACHINE LEARNING USED?
RECENT ADVANCEMENTS IN DATA ANALYSIS
INTRODUCTION TO STATISTICS
CHOOSING THE RIGHT KIND OF MODEL FOR MACHINE
LEARNING
SUPERVISED LEARNING
CLASSIFICATIONS
UNSUPERVISED LEARNING
NEURAL NETWORKS
REINFORCEMENT LEARNING
ENSEMBLE MODELING
THINGS YOU MUST KNOW FOR MACHINE LEARNING
PROGRAMMING TOOLS
DEVELOPING MODELS
AFTERWORD
MACHINE LEARNING MATHEMATICS
INTRODUCTION
CHAPTER 1: INTRODUCTION TO MACHINE LEARNING
CHAPTER 2: MACHINE LEARNING ALGORITHMS
CHAPTER 3: NEURAL NETWORK LEARNING MODELS
CHAPTER 4: LEARNING THROUGH UNIFORM
CONVERGENCE
CHAPTER 5: DATA SCIENCE LIFECYCLE AND
TECHNOLOGIES
CONCLUSION
Y F G
OUR REE IFT
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MACHINE LEARNING FOR
BEGINNERS
A Math Guide to Mastering Deep Learning
and Business Application. Understand How
Artificial Intelligence, Data Science, and
Neural Networks Work Through Real
Examples
Introduction
Congratulations on purchasing Machine Learning for Beginners and thank
you for doing so.
There are many opportunities opening up in the field of machine learning.
It’s being adopted as a tool by almost every major industry. Whether you are
interested in health care, business and finance, agriculture, clean energy, and
many others, there is someone utilizing the power of machine learning to
make their job easier.
Unfortunately for these industries, but fortunate for you is that there is a
major shortage of talent in the field of data science and artificial intelligence.
While entry-level data science jobs remain competitive, there is a major
shortage of experienced data professionals who can fill the high-level roles.
It’s a newer field in computer science, with a younger group of individuals
who make up for much of the field.
It can be very financially rewarding if you manage to land a job in data
science. In 2016 the average data scientist made about $111,000, with
predicted growth over the next five years. About half of data scientists
working in the field have a Ph.D. It’s not a requirement, but it’s something to
consider if you are looking into starting a real career as a data scientist.
If you are looking to add machine learning to your wheelhouse, so that you
can have a better understanding of it and implement it in your own business
or projects down the road, then a Ph.D. may not be necessary. But for those
looking to enter the field, higher education is recommended as it will help
you stand out amongst the field.
Indeed.com called machine learning the best career in 2019, and it’s easy
to see why. With a huge demand for talented data scientists and a lucrative
payout, it's worth a look. And big data doesn’t seem to be going away
anytime soon with an increase in connectivity and higher than ever internet
usage by both consumers and companies alike. Data is a part of our modern
world, and as the complexity and size of data increases, it will take even more
specialized knowledge and skills to be able to complete the task at hand.
To supplement the knowledge in this book, I highly recommend seeking
further knowledge in statistics and programming. A good base of statistical
knowledge is required to perform any work in machine learning because
statistical mathematics provides the structure and justification for all the
models and algorithms that data scientists use for machine learning.
The Purpose of This Book
This book is not meant to be a comprehensive textbook on machine
learning. Instead, it will give you a base of knowledge to continue with your
study of machine learning and artificial intelligence. In order to continue your
studies and master the subject, there is a large degree of studying that must be
done. Will discuss the general structure and organization of machine learning
models, the common terms, and the basic statistical concepts necessary to use
and understand machine learning.
It’s necessary to have a solid understanding of statistics and quantitative
analysis to be a data scientist. After all, artificial intelligence and machine
learning are rooted in statistics. This provides the anchor and foundation for
the kind of mathematics needed.
While coding is not required to understand this book, it is a major
component of machine learning. In order to handle large volumes of data,
data scientists need to have a working knowledge of computer programming
to ‘tell’ the data what they want it to do. This book will not offer much in the
way of coding information, but it will present resources and avenues to get
you started in studying coding on your own. By the end of the book, I will at
least assist you in setting up Python with the necessary libraries and toolkits
to help you start learning to code.
The most common language used in machine learning is Python. It’s a