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Optimize Models with Optuna: Adaptive Hyperparameter Tuning

BeginnerGuided Project

Learn adaptive hyperparameter tuning with Optuna using PyTorch and scikit-learn to optimize machine learning models for better performance. In this guided project, you’ll explore how automated hyperparameter optimization improves a wide range of machine learning workflows. You’ll work hands-on with the Optuna framework to define search spaces, evaluate trials, and apply pruning to cut training time and computational costs. This project breaks down complex concepts into clear tasks, allowing you to focus on learning in a fully online environment without setup or configuration hassles.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • Machine Learning, Hyperparameter Tuning, Optimization

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 20 mins

Platform

  • SkillsNetwork

Last Update

  • January 7, 2026
About this Guided Project

Why This Project Matters

Hyperparameter tuning plays a critical role in determining how well machine learning models perform, yet it is often handled manually through trial and error. As models increase in complexity, this approach becomes inefficient, time-consuming, and difficult to scale. Optuna addresses this challenge by providing an automated, intelligent framework for hyperparameter optimization that adapts based on past results.

In this guided project, you’ll learn how to move beyond default model settings and manual tuning. By working with both scikit-learn and PyTorch, you’ll gain hands-on experience optimizing machine learning models and neural networks using the industry-relevant tool, Optuna. Completing this project will help you build practical optimization skills that are directly applicable to real-world machine learning workflows.

A Look at the Project Ahead

After completing this project, you will be able to:
  • Apply Optuna to optimize machine learning models by defining search spaces.
  • Run and evaluate optimization trials to identify optimal model configurations.
  • Tune and evaluate models built with scikit-learn and PyTorch.
  • Use automated optimization and pruning to improve performance and efficiency.
  • Analyze optimization results using Optuna's built-in visualization tools.

What You'll Need

To get started with this guided project, you should have:
  • Proficiency with Python
  • Familiarity with PyTorch and scikit-learn libraries
  • Introductory knowledge of machine learning concepts (model training and evaluation)
  • A modern web browser (Chrome, Edge, Firefox)
No local installation or setup is required. The IBM Skills Network Labs environment is fully hosted online and comes with all necessary tools and libraries, allowing you to focus entirely on learning and experimentation without configuration hassles.

Instructors

Joshua Zhou

Data Scientist

I like building fun and practical things.

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Contributors

Zikai Dou

Data Scientist at IBM

Ph.D. Candidate in Computer Science at McMaster University, specializing in Federated Learning (FL), Graph Neural Networks (GNNs), and Computer Vision (CV). I develop privacy-preserving, distributed AI systems that tackle real-world challenges in healthcare, finance, and enterprise applications. Passionate about bridging academic research with industry impact to advance scalable and trustworthy AI.

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Abdul Fatir

Data Scientist

Abdul specializes in Data Science, Machine Learning, and AI. He has deep expertise in understanding how the latest technologies work, and their applications. Feel free to contact him with questions about this project or any other AI/ML topics.

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