Book description
The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.
If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud.
- Explore ways that predictive analytics can provide direct input back to your business
- Understand mathematical tools commonly used in predictive analytics
- Learn the development frameworks used in predictive analytics applications
- Appreciate the role of predictive analytics in the machine learning process
- Examine industry implementations of predictive analytics
- Build, train, and retrain predictive models using Python and TensorFlow
Publisher resources
Table of contents
- Preface
- 1. Data Analytics in the Modern Enterprise
- 2. Predictive Analytics: An Operational Necessity
- 3. The Mathematics and Algorithms Behind Predictive Analytics
- 4. Working with Data
- 5. Python and scikit-learn for Predictive Analytics
- 6. TensorFlow and Keras for Predictive Analytics
- 7. Predictive Analytics for Business Problem-Solving
- 8. Exploring AWS Cloud Provider Services for AI/ML
- 9. Food for Thought
- Index
- About the Author
Product information
- Title: Predictive Analytics for the Modern Enterprise
- Author(s):
- Release date: May 2024
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098136864
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