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Bayesian Machine Learning for Optimization in Python

Gain insights into Bayesian optimization and statistical modeling to efficiently tackle high-dimensional problems. Learn about hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

Intermediate

32 Lessons

8h

Certificate of Completion

Gain insights into Bayesian optimization and statistical modeling to efficiently tackle high-dimensional problems. Learn about hyperparameter tuning, experimental design, algorithm configuration, and system optimization.
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Explanations

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This course includes

1 Project
3 Assessments
49 Playgrounds
Course Overview
What You'll Learn
Course Content
Recommendations

Course Overview

Bayesian optimization allows developers to leverage Bayesian inference and statistical modeling to efficiently search for the optimal solution in a high-dimensional space. Starting with the fundamentals of statistics and Bayesian statistics, you’ll explore different concepts of machine learning and its applications in software engineering. Next, you’ll discover different strategies for optimizations. Through practical examples and hands-on exercises, you’ll gain proficiency in implementing Bayesian optimi...Show More
Bayesian optimization allows developers to leverage Bayesian inference and statistical modeling to efficiently search for the op...Show More

TAKEAWAY SKILLS

Python

Python 3

Python Programming

Data Science

Machine Learning

Machine learning paradigms

Optimization

Neural Networks

Deep neural networks

Deep Learning

What You'll Learn

An understanding of Bayes’ theorem and its applications
Familiarity with the core components of Bayesian machine learning and its application to optimization
Hands-on experience tuning hyperparameters using Bayesian optimization
The ability to attain optimized solutions for complex problems using Bayesian statistics
Familiarity with core components of the Dragonfly framework for scalable Bayesian optimization
An understanding of Bayes’ theorem and its applications

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Course Content

5.

Optimization: An Overview

6 Lessons

Work your way through optimization techniques, including linear, random search, evolutionary, and Bayesian methods, with practical use cases.

9.

Conclusion

1 Lessons

Finish the course with a robust grasp of Bayesian optimization principles, tools, and real-world applications.

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