Machine Learning for Absolute Beginners: An Introduction to the Fundamentals and Applications of Machine Learning

By daniel huston

Machine Learning for Absolute Beginners" is a book
designed to introduce readers with no prior
experience to the exciting and rapidly growing field
of machine learning. Machine learning is a branch of
artificial intelligence that enables computers to learn
from data and make predictions or decisions based
on that learning.
This book is written in a clear and approachable
style, making it easy for readers to understand the
core concepts and techniques of machine learning. It
assumes no prior knowledge of the subject, and
starts from the very basics, gradually building up the
reader's understanding of the field.
The book covers a wide range of topics, including
data preprocessing, classification, regression,
clustering, and deep learning. It also includes
practical examples and hands-on exercises that allow
readers to apply what they've learned and gain realworld experience in machine learning.
Whether you are a student, a professional, or just
someone interested in learning about machine
learning, this book provides a solid foundation for
understanding the fundamentals of this exciting
field. By the end of the book, readers will have a
4
strong understanding of the concepts and techniques
of machine learning and will be well-equipped to
tackle more advanced topics in the future.

• Language: English
• Publisher: Lulu.com
• Release date: May 1, 2023
• ISBN: 9781447720621

Book preview

Machine Learning for Absolute Beginners

An Introduction to the Fundamentals and Applications of Machine Learning

Daniel Huston

Introduction

Machine Learning for Absolute Beginners" is a book designed to introduce readers with no prior experience to the exciting and rapidly growing field of machine learning. Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions based on that learning.

This book is written in a clear and approachable style, making it easy for readers to understand the core concepts and techniques of machine learning. It assumes no prior knowledge of the subject, and starts from the very basics, gradually building up the reader's understanding of the field.

The book covers a wide range of topics, including data preprocessing, classification, regression, clustering, and deep learning. It also includes practical examples and hands-on exercises that allow readers to apply what they've learned and gain real-world experience in machine learning.

Whether you are a student, a professional, or just someone interested in learning about machine learning, this book provides a solid foundation for understanding the fundamentals of this exciting field. By the end of the book, readers will have a strong understanding of the concepts and techniques of machine learning and will be well-equipped to tackle more advanced topics in the future.

I

Introduction to Machine Learning

What is Machine Learning?

Applications of Machine Learning

Types of Machine Learning

II

What is Supervised Learning?

Regression about Supervised Learning

Classification

III

What is Unsupervised Learning?

Clustering about Unsupervised Learning

Association Rule

IV

Reinforcement Learning

What is Reinforcement Learning?

Components of Reinforcement Learning

Applications of Reinforcement Learning

V

Python Libraries for Machine Learning

Popular Machine Learning Frameworks

Machine Learning in the Cloud

VI

Importance of Ethical Considerations in Machine Learning

Bias and Fairness in Machine Learning

Privacy and Security in Machine Learning

VII

Recap of Machine Learning Fundamentals

Future of Machine Learning

Final Thoughts for Absolute Beginners

I

Introduction to Machine Learning

What is Machine Learning?

Machine learning is a subfield of artificial intelligence that allows computers to learn from data without being explicitly programmed. The goal of machine learning is to enable machines to automatically improve their performance on a given task as they are exposed to more data. This technology is revolutionizing many industries, from healthcare to finance, and is expected to continue to grow and develop in the coming years.

Machine learning is based on the idea that computers can learn from data, just as humans do. In order to teach a machine how to perform a task, we need to provide it with a dataset of examples that represent that task. The machine then uses statistical methods to analyze the data and identify patterns that are relevant to the task at hand.

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine is trained on a labeled dataset to make predictions on new data. For example, if we want to train a machine to predict whether an email is spam or not, we would provide the machine with a labeled dataset of emails that are either spam or not spam. The machine would then learn to classify new emails as either spam or not spam based on the patterns it finds in the data.

In unsupervised learning, the machine is not given labeled data. Instead, it is given a dataset and asked to find patterns or groupings on its own. For example, if we want to group customers based on their shopping behavior, we would provide the machine with a dataset of