Bayesian Machine Learning for Optimization in Python

Learn Bayesian optimization and statistical modeling to tackle high-dimensional problems. Explore hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

Intermediate

32 Lessons

8h

Certificate of Completion

Learn Bayesian optimization and statistical modeling to tackle high-dimensional problems. Explore hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project
3 Assessments
49 Playgrounds

This course includes

1 Project
3 Assessments
49 Playgrounds

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

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

What You'll Learn

An understanding of Bayes’ theorem and its applications

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Course Content

1.

Introduction to Bayesian Statistics

Learn how to use Bayesian statistics to integrate prior knowledge and handle uncertainty.

Bayesian Statistics: Knowledge Check

Assessment

2.

Bayesian Machine Learning

Look at how Bayesian methods enhance machine learning by integrating prior knowledge for robust, probabilistic predictions.

Regression Using Bayes’ Theorem: Knowledge Check

Assessment

3.

Optimization: An Overview

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

Optimization: Knowledge Check

Assessment

4.

Bayesian Optimization: From Scratch

Grasp the fundamentals of deploying Bayesian optimization for efficient, complex problem-solving and function evaluations.

Hyperparameter Tuning Using Bayesian Optimization

Project

5.

Conclusion

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

Overcoming Uncertainty with Bayesian Probability in Python

Project

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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