This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition-as well as some we don´t yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as ´´Big Data´´ has gotten bigger, the theory of machine learning-the foundation of efforts to process that data into knowledge-has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You´ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading this book, you will understand how to use PySpark´s machine learning library to build and train various machine learning models. Additionally you´ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. What You Will Learn Build a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model Who This Book Is For Data science and machine learning professionals.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject
Step into the world of Ray Kurzweil, the ´´restless genius´´ (Wall Street Journal) and ´´ultimate thinking machine´´ (Forbes), whose predictions for an age in which man and machine are interchangeable are startling, provocative -- and closer to realization than you think. Imagine a world where the difference between man and machine blurs, where the line between humanity and technology fades, and where the soul and the silicon chip unite. This is not science fiction. This is the twenty-first century according to Ray Kurzweil, the inventor of the most innovative and compelling technology of our era. In his inspired hands, life in the new millennium no longer seems daunting. Instead, it promises to be an age in which the marriage of human sensitivity and artificial intelligence fundamentally alters and improves the way we live. More than just a list of predictions, Kurzweil´s prophetic blueprint for the future guides us through the inexorable advances that will result in: computers exceeding the memory capacity and computational ability of the human brain by the year 2020 (with human-level capabilities not far behind); relationships with automated personalities who will be our teachers, companions, and lovers; and information fed straight into our brains along direct neural pathways. Eventually, the distinction between humans and computers will have become sufficiently blurred that when the machines claim to be conscious, we will believe them.
Weighing in from the cutting-edge frontiers of science, today´s most forward-thinking minds explore the rise of ´´machines that think.´´ Stephen Hawking recently made headlines by noting, ´´The development of full artificial intelligence could spell the end of the human race.´´ Others, conversely, have trumpeted a new age of ´´superintelligence´´ in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org (´´the world´s smartest website´´ - The Guardian), asked the world´s most influential scientists, philosophers, and artists one of today´s most consequential questions: What do you think about machines that think?
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.