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Supervised Machine Learning in Security Applications
Supervised Machine Learning in Security Applications
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 2.5 Hours | 4.32 GB
Genre: eLearning | Language: English

arguably the most important field in the IT industry is security. With more and more commerce and business being conducted online every day, keeping data safe by detecting and repelling attacks is paramount to every organization. One area that shows great potential in the battle against hackers and their exploits is machine learning. Unleashing the increasing power and finesse of these systems toward defeating intrusions and data theft is no longer a theoretical pursuit. Indeed, machine learning is being used to defend systems and networks across an increasing range of industries and enterprises, so it's no mystery that there's also an increasing demand for skilled and qualified security specialists who can apply data science techniques to the task of data security.

This video course introduces you to machine learning and explains the concept at the core of machine learning, models, and how you "train" them to perform tasks and solve problems. This video course focuses specifically on "supervised" training, or learning, in a security context. Your host, cyber security specialist and data scientist Charles Givre, provides examples and use cases that use real security data and focus on actual applications of machine learning to security problems rather than contrived or superficial datasets. You will see how to build supervised machine learning models, evaluate and optimize their performance, and then apply these models in a security context. You will examine the theory and implementation behind the supervised machine learning techniques most relevant to security, including random forest, support vector machines, and more.

This video course is one in a set of three individual ones intended for security professionals who want to learn how to use and apply data science to their toughest security problems. Mr. Givre focuses on the tools and techniques that are directly applicable to the industry, and uses security problems and datasets to walk you through the entire data science process from end-to-end.

Supervised Machine Learning in Security Applications
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Tags: supervised, machine, learning, security, applications

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