Название: Introduction to Machine Learning with Python
Автор: Deepti Chopra, Roopal Khurana
Издательство: Bentham Books
Год: 2023
Страниц: 139
Язык: английский
Формат: pdf (true), epub
Размер: 27.3 MB

Machine Learning is a subfield of Artificial Intelligence (AI), 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.

Machine learning has become part and parcel of day-to-day private/non-profit/business and government operations because of its ability to grasp automatically through past experiences without being explicitly programmed. Today, machine learning has conquered the entire industry due to its numerous applications ranging from digital marketing to space research. Today, it governs the industry in terms of building high-tech products, ranking web searches, building speech recognition systems, recommendation systems, etc. However, we have not yet developed fully operational machines that give judgments on their own like humans but it is not far away to reach that level. From this book, we intend to re-discover the core concepts of Machine learning paradigms along with numerous architectures and algorithms used in different paradigms.

The book elaborates on various topics related to the implementation side using Python with real-life examples. The book can kickstart your career in the field of Machine Learning. It also provides the basic knowledge of Python which is a prerequisite of this course. We can say that this book is meant for neophyte users who wish to get acquainted with the implementation of machine learning using Python. The reader will be able to read well-explained examples and exercises and it will be an ideal choice for Machine Learning enthusiasts. The book presents detailed practice exercises for offering a comprehensive introduction to machine learning techniques along with the basics of Python. The book leverages algorithms of machine learning in a unique way of describing real-life applications. Though not mandatory, some experience with subject knowledge will fasten the learning process.

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