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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Data preparation

The next step is a data transformation tier that processes the raw data; some of the transformations that need to be done are:

  • Data Cleansing
  • Filtration
  • Aggregation
  • Augmentation
  • Consolidation
  • Storage

The cloud providers have become the major data science platforms. Some of the most popular stacks are built around:

  • Azure ML service
  • AWS SageMaker
  • GCP Cloud ML Engine
  • SAS
  • RapidMiner
  • Knime

One of the most popular tools to perform these transformations is Apache Spark, but it still needs a data store. For persistence, the most common solutions are:

  • Hadoop Distributed File System (HDFS)
  • HBase
  • Apache Cassandra
  • Amazon S3
  • Azure Blob Storage

It's also possible to process data for machine learning in-place, inside the database; databases like SQL Server and SQL Azure are adding specific machine learning functionality to support machine learning pipelines. Spark has that...