Book Image

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Alberto Artasanchez, 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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Feature engineering

According to a recent survey performed by the folks at Forbes, data scientists spend around 80% of their time on data preparation:*-dn9U8gMVWjDahQV.jpg

Figure 4: Breakdown of time spent by data scientists (source: Forbes)

This statistic highlights the importance of data preparation and feature engineering in data science.

Just like judicious and systematic feature selection can make models faster and more performant by removing features, feature engineering can accomplish the same by adding new features. This seems contradictory at first blush, but the features that are being added are not features that were removed by the feature selection process. The features being added are features that might have not been in the initial dataset. You might have the most powerful and well-designed machine learning algorithm in the world, but if your input features are not relevant, you will never be able to produce useful results. Let's analyze a couple of simple examples to get...