April 2018
Beginner to intermediate
282 pages
6h 52m
English
Content preview from Hands-On Automated Machine Learning
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Summary
In this chapter, you learned about many different aspects when it comes to choosing a suitable ML pipeline for a given problem.
Computational complexity, differences in training and scoring time, linearity versus non-linearity, and algorithm, specific feature transformations are valid considerations and it’s useful to look at your data from these perspectives.
You gained a better understanding of selecting suitable models and how machine learning pipelines work by practicing various use cases. You are starting to scratch the surface and this chapter was a good starting point to extend these skills.
In the next chapter, you will learn about optimizing hyperparameters and will be introduced to more advanced concepts, such as Bayesian-based ...
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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.