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

Introduction to Statistics
Statistics is the mathematical science of data. It is a practice by which data
is collected, observed, and analyzed in order to infer meaning and examine
quantifiable relationships between different variables. Machine learning is a
form of inferential statistics, meaning that by examining the relationship
between variables, we should be able to come up with predictions for new
variables.
Statistics is used in a wide variety of disciplines. It's used in biology to
study and examines animal and plant life. It has wide applications in the

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business world from making stock market forecasts to analyzing consumer
behavior. Economists use statistics to explain quantifiable patterns in world
markets. In medicine, statistics can be used to improve the way that doctors
and disease specialists look at the spread and prevention of disease.
Statistics make up the core of machine learning. If you aren’t willing to dive
into statistics, then machine learning isn’t for you. Machine learning uses
statistical algorithms to help computers learn. Machine learning is all about
the tracking of data and how computers can use data to improve themselves.
There are two types of statistics which are relevant to this book. The first
one is descriptive analysis, which you might use during the beginning of
your modeling process to look for indicators in your data. But most of what
we do in machine learning falls into a different category called predictive
analysis.
Key term; Descriptive analysis. The descriptive analysis helps us examine
we are right now. Looking at our current situation in context to the past and
seeing why things are the way they are. Why do some things sell better than
others? What trends are we seeing in products currently on the market?