HomeCoursesBayesian Machine Learning for Optimization in Python

5.0

Intermediate

Updated 1 month ago

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.

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Overview

Content

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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 optimization algorithms and fine-tuning them for specific tasks. By the end of the course, you’ll have a comprehensive understanding of the entire Bayesian optimization workflow, from problem formulation to solution optimization. By completing this course, you’ll be able to tackle complex optimization problems more efficiently and effectively. You’ll be equipped to find optimal solutions in areas such as hyperparameter tuning, experimental design, algorithm configuration, and system optimization.

Bayesian optimization allows developers to leverage Bayesian inference and statistical modeling to efficiently search for the op...Show More

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

TAKEAWAY SKILLS

Python

Python 3

Python Programming

Data Science

Machine Learning

Machine learning paradigms

Optimization

Neural Networks

Deep neural networks

Deep Learning

Content

32 Lessons1 Project1 Quiz3 Assessments3 Challenges

Introduction to Bayesian Statistics

9 Lessons

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

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

7 Lessons

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

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

6 Lessons

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

Optimization: Knowledge Check

Assessment

Bayesian Optimization: From Scratch

9 Lessons

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

Hyperparameter Tuning Using Bayesian Optimization

Project

Conclusion

1 Lessons

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

Overcoming Uncertainty with Bayesian Probability in Python

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Danial Amin

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

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AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

Explain with AI

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AI Code Mentor

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