May 2018
Beginner to intermediate
364 pages
7h 43m
English
Content preview from Hands-On Data Science with Anaconda
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Summary
In this chapter, we have discussed supervised learning, such as classification, the k-nearest neighbors algorithm, Bayes classifiers, reinforcement learning, and the RTextTools and sklearn modules in R. In addition, we discussed implementations of supervised learning via R, Python, Julia, and Octave.
For the next chapter, we will discuss predictive data analytics, modeling and validation, some useful datasets, time-series analytics, how to predict the future, seasonality, and how to visualize our data. For Python packages, we will mention predictives-models-building, model-catwalk, and easyML. For R packages, we will discuss datarobot, LiblineaR, eclust, and AppliedPredictiveModeling. For Julia packages, we will explain EmpiricalRisks ...
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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.