Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Preprocessing data

Raw data is the fuel of machine learning algorithms. But just like we cannot put crude oil into a car and instead we must use gasoline, machine learning algorithms expect data to be formatted in a certain way before the training process can begin. In order to prepare the data for ingestion by machine learning algorithms, the data must be preprocessed and converted into the right format. Let's look at some of the ways this can be accomplished.

For the examples we will analyze to work, we will need to import a few Python packages:

import numpy as np
from sklearn import preprocessing

Also, let's define some sample data:

input_data = np.array([[5.1, -2.9, 3.3],
                       [-1.2, 7.8, -6.1],
                       [3.9, 0.4, 2.1],
                       [7.3, -9.9, -4.5]])

These are the preprocessing techniques we will be analyzing:

  • Binarization
  • Mean removal
  • Scaling
  • Normalization