December 2018
Intermediate to advanced
318 pages
8h 28m
English
Content preview from Hands-On Machine Learning for Cybersecurity
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Summary
In this chapter, we used machine learning to detect financial fraud by handling imbalanced datasets. We also covered random under-sampling and oversampling. We looked at SMOTE as well as the modified version of SMOTE. Then we learned about detecting credit card fraud, which includes the logistic regression classifier and tuning hyperparameters.
This chapter also explained deep learning time as well as the Adam gradient optimizer. In the next chapter, we will explore a few different cybersecurity case studies.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.