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AI-Powered Time Series Forecasting with Python
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 11m | 328 MB
Instructor: Tobias Zwingmann
For any business, gaining and understanding insights into future trends, customer demands, or market conditions is an important factor in success. And with the wide availability of machine learning and artificial intelligence tools, thousands of businesses are able to enhance their operations through time series forecasting. In this course, Tobias Zwingmann introduces you to time series forecasting using Python and AI and shows how you can apply them to your business. Learn how to translate forecasting workflows from static, classroom problems to dynamic, real-time use cases. Plus, find out about the tools and approaches you can apply to other AI and machine learning tasks.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out the "GitHub Codespaces" video to learn how to get started.
Learning objectives
[list]
[*]Learn the differences between batch and online prediction, and what each is best suited for.
[*]Discover the various types of features that go into a real-time model-online, batch, session-based, and more-including how to calculate and store each given our example.
[*]Learn about real-time vs. near real-time prediction and the differences between both paradigms and when to use each.
[*]Learn the importance of model monitoring and how to effectively use it to keep models fresh.
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Exercise Files on GitHub
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