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Zachary Glassman offers a foundation in building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline, from prototyping to production. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using real-world datasets.

The TensorFlow library enables the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Dana Mastropole details TensorFlow's capabilities through its Python interface.

To handle real-time big data, you need to solve two difficult problems: How do you ingest that much data, and how will you process that much data? Jesse Anderson explores the latest real-time frameworks (both open source and managed cloud services), discusses the leading cloud providers, and explains how to choose the right one for your company.

Behzad Bordbar demonstrates how to implement typical data science workflows using Apache Spark. You'll learn how to wrangle and explore data using Spark SQL DataFrames and how to build, evaluate, and tune machine learning models using Spark MLlib.

Jean Innes, Matthew Ward, Emanuele Haerens, and Alli Paget lead a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.