August 2019
Intermediate to advanced
342 pages
9h 35m
English
Content preview from Hands-On Artificial Intelligence for Cybersecurity
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Min–max scaler
In the following code, we see an example of feature engineering using the MinMaxScaler class of scikit-learn, aimed at scaling features to lie between a given range of values (minimum and maximum), such as 0 and 1:
from sklearn import preprocessing import numpy as np raw_data = np.array([ [ 2., -3., 4.], [ 5., 0., 1.], [ 4., 0., -2.]]) min_max_scaler = preprocessing.MinMaxScaler() scaled_data = min_max_scaler.fit_transform(raw_data)
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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.