July 2017
Beginner to intermediate
715 pages
17h 3m
English
Content preview from Java: Data Science Made Easy
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Search engine service
Let us finally come back to our running example--building a search engine. In Chapter 7, Extreme Gradient Boosting, we created a ranking model, which we can use for reordering search engine results so that the most relevant content gets higher positions.
In the previous chapter, Chapter 9, Scaling Data Science, we extracted a lot of text data from Common Crawl. What we can do now is to put it all together--use Apache Lucene to index the data from Common Crawl, and then search its content and get the best results with the XGBoost ranking model.
We already know how to use Hadoop MapReduce to extract text information from Common Crawl. However, if you remember, our ranking model needs more than just text--apart from just ...
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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.