Introduction
Machine learning is a subfield of artificial intelligence, broadly defined as a
machine's capability to imitate intelligent human behavior. Like humans, machines
become capable of making intelligent decisions by learning from their past
experiences. Machine learning is being employed in many applications, including
fraud detection and prevention, self-driving cars, recommendation systems, facial
recognition technology, and intelligent computing. This book helps beginners learn
the art and science of machine learning. It presens real-world examples that
leverage the popular Python machine learning ecosystem,
The topics covered in this book include machine learning basics: supervised and
unsupervised learning, linear regression and logistic regression, Support Vector
Machines (SVMs). It also delves into special topics such as neural networks, theory
of generalisation, and bias and fairness in machine learning. After reading this
book, computer science and engineering students - at college and university levels -
will receive a complete understanding of machine learning fundamentals and will be
able to implement neural network solutions in information systems, and also extend
them to their advantage