January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Summary
In this chapter, we learned about time series analysis and white noise. We were introduced to the concepts of random walk, autoregression, autocorrelation, and stationarity, which describes how to figure out whether data is stationary.
We also learned about differencing, taking the time series data and computing the differences between consecutive observations that lead to the formation of a new series. This chapter also talked about the AR model, which is a part of a stochastic process wherein the specific lagged values of yt are used as predictor variables and regressed on yt in order to estimate the values. We also learned two optimization parameters, namely the AR model and ARIMA model.
In the next chapter, we will learn about ...
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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.