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Global Cyber Security Labor Shortage and International Business Risk
Global events involving cybersecurity breaches have highlighted the ever-growing dependence on interconnected online systems in international business. The increasing societal dependence on information technology has pushed cybersecurity to the forefront as one of the most urgent challenges facing the global community today. Poor cybersecurity is the primary reason hackers are able to penetrate safeguards in business computers and other networks, and the growing global skills gap in cybersecurity simply exacerbates the problem. Global Cyber Security Labor Shortage and International Business Risk provides emerging research exploring the theoretical and practical aspects of protecting computer systems against online threats as well as transformative business models to ensure sustainability and longevity. Featuring coverage on a broad range of topics such as cybercrime, technology security training, and labor market understanding, this book is ideally designed for professionals, managers, IT consultants, programmers, academicians, and students seeking current research on cyber security’s influence on business, education, and social networks.
Hands-On Data Science and Python Machine Learning
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
Kinematics and Dynamics of Mechanical Systems
Effectively Apply the Systems Needed for Kinematic, Static, and Dynamic Analyses and DesignA survey of machine dynamics using MATLAB and SimMechanics, Kinematics and Dynamics of Mechanical Systems: Implementation in MATLAB and SimMechanics combines the fundamentals of mechanism kinematics, synthesis, statics and dynamics with real-world application