PDF Read Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlow Full Book By Gabriel Rhys

Book Synopsis Can Machines Really Learn? Machine learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning has become an essential pillar of IT in all aspects, even though it has been hidden in the recent past. We are increasingly being surrounded by several machine learning-based apps across a broad spectrum of industries. From search engines to anti-spam filters to credit card fraud detection systems, list of machine learning applications is everexpanding in scope and applications. The goal of this book is to provide you with a hands-on, project-based overview of machine learning systems and how they are applied over a vast spectrum of applications that underpins AI technology from Absolute Beginners to Experts. This book is a fast-paced, thorough introduction to Machine Learning that will have you writing programs, solving problems, and making things that work in no time. This book presents algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including: Supervised and Unsupervised learning methodsArtificial Neural NetworksHands-on projects based on Real-world applicationsBayesian learning methodReinforcement learningAnd much more By the end of this book, you should have a strong understanding of machine learning so that you can

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Author : Gabriel Rhys Pages : 178 pages Publisher : CreateSpace Independent Publishing Platform 2017-10-18 Language : English ISBN-10 : 1978373880 ISBN-13 : 9781978373884

pursue any further and more advanced learning. Learning Outcomes: By the end of this book, you will be able to: Identify potential applications of machine learning in practice Describe the core differences in analyses enabled by regression, classification, and clustering Select the appropriate machine learning task for a potential application Apply regression, classification, and clustering Represent your data as features to serve as input to machine learning models Utilize a dataset to fit a model to analyze new data Build an end-to-end application that uses machine learning at its core Implement these techniques in Python If you ve been thinking seriously about digging into ML, this book will get you up to speed. Why wait any longer?