September 2016
Beginner to intermediate
264 pages
9h 26m
English
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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.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.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.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.
Chapter 10. Example: digital display advertising
This chapter covers
- Visualizing and preparing a real-world dataset
- Building a predictive model of the probability that users will click a digital display advertisement
- Comparing the performance of several algorithms in both training and prediction phases
- Scaling by dimension reduction and parallel processing
Chapter 9 presented techniques that enable you to scale your machine-learning workflow. In this chapter, you’ll apply those techniques to a large-scale real-world problem: optimizing an online advertising campaign. We begin with a short introduction to the complex world of online advertising, the data that drives it, and some of the ways it’s used by advertisers to maximize return on ...
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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.