Giuseppe Bonaccorso - Machine Learning Algorithms Popular algorithms for data science and machine learning

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First published: July 2017
Second edition: August 2018

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To my family and to all the people who always believed in me and encouraged me in this long journey!

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Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MScEng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

Doug Ortiz is an experienced enterprise cloud, big data, data analytics, and solutions architect who has architected, designed, developed, re-engineered, and integrated enterprise solutions. Other expertise includes Amazon Web Services, Azure, Google Cloud, business intelligence, Hadoop, Spark, NoSQL databases, and SharePoint, to name a few.

He is the founder of Illustris, LLC and is reachable at dougortiz@illustris.org.

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This book is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all the data scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering, and natural language processing. In order to facilitate the learning process, all theoretical elements are followed by concrete examples based on Python.

A basic but solid understanding of this topic requires a foundation in mathematics, which is not only necessary to explain the algorithms, but also to let the reader understand how it's possible to tune up the hyperparameters in order to attain the best possible accuracy. Of course, it's impossible to cover all the details with the appropriate precision. For this reason, some topics are only briefly described, limiting the theory to the results without providing any of the workings. In this way, the user has the double opportunity to focus on the fundamental concepts (without too many mathematical complications) and, through the references, examine in depth all the elements that generate interest.

The chapters can be read in no particular order, skipping the topics that you already know. Whenever necessary, there are references to the chapters where some concepts are explained. I apologize in advance for any imprecision, typos or mistakes, and I'd like to thank all the Packt editors for their collaboration and constant attention.

This book is for machine learning engineers, data engineers, and data scientists who want to build a strong foundation in the field of predictive analytics and machine learning. Familiarity with Python would be an added advantage and will enable you to get the most out of this book.

, A Gentle Introduction to Machine Learning, introduces the world of machine learning, explaining the fundamental concepts of the most important approaches to creating intelligent applications and focusing on the different kinds of learning methods.

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