Bayesian Machine Learning for Optimization in Python

Intermediate

32 Lessons

Certificate of Completion

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

Course Content

Introduction to Bayesian Statistics

An Overview of the Course An Introduction to Statistics Frequentist vs. Bayesian Statistics Maximum Likelihood Estimation and Its Applications Bayes’ Theorem Posteriority and Bias in Probability Applications of Bayes’ Theorem in Software Engineering Advantages of Bayes’ Theorem over Deterministic Statistics Bayes’ Theorem in Machine Learning

Bayesian Statistics: Knowledge Check

Assessment

Bayesian Machine Learning

What is Machine Learning?Incorporating Bayes’ Theorem in Machine Learning Techniques to Estimate Model Parameters Gaussian Processes Regression using Bayes’ Theorem Understanding the Theory of Bayesian Regression Application of Bayesian Regression

Regression Using Bayes’ Theorem: Knowledge Check

Assessment

Optimization: An Overview

Introduction to Optimization Linear Optimization Optimization Methods: Random Search Optimization Evolutionary Algorithms Optimization: Bayesian Methods Optimization Use Cases in Software Engineering

Optimization: Knowledge Check

Assessment

Bayesian Optimization: From Scratch

Components of Bayesian Optimization Business Case Study: Optimizing Packaging Coding: Bayesian Optimization from Scratch Black Box Bayesian Optimization Important Libraries and Frameworks Bayesian Optimization Using Dragonfly Getting to Know Dragonfly Better Comparing Bayesian Optimization with Other Optimization Methods Coding Challenge: Bayesian Optimization

Hyperparameter Tuning Using Bayesian Optimization

Project

Conclusion

Wrap Up

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

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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