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)
24
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25
Index

Big data and machine learning

Big data technologies are leveraged successfully by technology companies around the world. Today's enterprises understand the power of big data, and they realize that it can be even more powerful when used in conjunction with machine learning.

Machine learning systems coupled with big data technology help businesses in a multitude of ways including managing, analyzing, and using the captured data far more strategically than ever before.

As companies capture and generate ever increasing volumes of data, this presents both a challenge and a great opportunity. Fortunately, these two technologies complement each other symbiotically. Businesses are constantly coming up with new models that increase the computational requirements of the resulting workloads. New advances in big data enable and facilitate the processing of these new use cases. Data scientists are seeing that current architectures can handle this increased workload. They are therefore...