September 2018
Intermediate to advanced
472 pages
12h 2m
English
book
Python Data Science Essentials - Third Edition
by Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti
Content preview from Python Data Science Essentials - Third Edition
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The Data Pipeline
Up until this point, we've explored how to load data into Python and process it to create a bidimensional NumPy array containing numerical values (your dataset). Now, we are ready to be immersed fully in data science, extract meaning from data, and develop potential data products. This chapter on data treatment and transformations and the next one on machine learning are the most challenging sections of this entire book.
In this chapter, you will learn how to do the following:
- Briefly explore data and create new features
- Reduce the dimensionality of data
- Spot and treat outliers
- Decide on the best score or loss metrics for your project
- Apply scientific methodology and effectively test the performance of your machine learning ...
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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.