Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Neural Networks Work Through Real
Examples

---

TABLE OF CONTENTS
 
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

---

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.

---