Hyperparameter Optimization Courses and Certifications

Hypertuning and Cross-Validation

Master hyperparameter optimization techniques through grid search, random search, and cross-validation methods to enhance machine learning model performance and achieve optimal results.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Hypertuning Classical Models

Master systematic hyperparameter optimization techniques using grid search, random search, and cross-validation strategies to transform weak ML baselines into high-performing models.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Coursera

Hyperparameter Tuning with Keras Tuner

70 ratings at Coursera

Learn to optimize Keras models using Keras Tuner, exploring algorithms like Random Search and Bayesian Optimization to automate hyperparameter tuning for improved machine learning performance.

• Coursera
• 1 hour 20 minutes
• Self-Paced
• Paid Course

Coursera

Optimize ML Models: Hyperparameter Tuning

Master hyperparameter tuning to optimize ML models using GridSearchCV, design effective search spaces, run systematic experiments, and interpret cross-validated results for real-world ML pipelines.

• Coursera
• 2 hours 30 minutes
• Self-Paced
• Paid Course

Learn industry-standard techniques for hyperparameter tuning in machine learning, including Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithms to improve model performance.

• Codecademy
• 1 hour
• Self-Paced
• Free Trial Available

Abhishek Thakur

Automated Hyperparameter Tuning for Deep Neural Networks

Comprehensive guide to automated hyperparameter tuning for deep neural networks using Optuna, covering dataset preparation, model design, and optimization techniques in PyTorch.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Databricks

Efficient Distributed Hyperparameter Tuning with Apache Spark

Learn to efficiently distribute hyperparameter tuning workloads using Apache Spark, avoiding common pitfalls and optimizing performance. Explore best practices with Hyperopt and joblib-spark for scaling ML model training.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Julia Silge

Hyperparameter Tuning Using Tidymodels

Explore hyperparameter tuning for random forest models using global food consumption data, enhancing model performance and gaining insights into international dietary patterns.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

AutoML Seminars

Hyperparameter Tuning on the Cloud - Optimizing Machine Learning Models with SEER and Rubberband

Master hyperparameter tuning optimization in cloud environments through SEER algorithm and Rubberband system, focusing on maximizing model accuracy while minimizing costs and time constraints.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Toronto Machine Learning Series (TMLS)

Hyperparameter Tuning with a Focus on Weights and Biases Sweeps

Explore hyperparameter tuning strategies and visualizations for deep learning models, focusing on Weights & Biases Sweeps. Accelerate progress in semantic segmentation and language understanding projects.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Anyscale

Hyperparameter Tuning with Ray Tune for Next-Gen Training Platform at LinkedIn

Explore LinkedIn's cloud-native deep learning platform and its use of Ray(Tune) for hyperparameter tuning in AI model training, aiming to democratize AI for engineers.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Anyscale

Reducing Cost, Latency, and Manual Efforts in Hyperparameter Tuning at Redicell

Optimize machine learning model training with Ray Tune. Reduce costs, latency, and manual efforts through efficient hyperparameter tuning, resource management, and experiment tracking.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

AutoML Seminars

Tuning LLM Judge Design Decisions for 1/1000 of the Cost

Discover how to systematically tune LLM judge hyperparameters using multi-objective multi-fidelity optimization to achieve better accuracy at 1/1000th the cost while ensuring reproducibility.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

AutoML Seminars

carps - A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks

Discover a comprehensive framework for evaluating hyperparameter optimization methods across diverse benchmark tasks, featuring 3336 tasks and 28 optimizer variants for standardized HPO research.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Association for Computing Machinery (ACM)

Understanding Parameter Spaces Using Local Optima Networks

Explores using Local Optima Networks to analyze parameter landscapes of Particle Swarm Optimization, revealing complex structures influenced by underlying objective functions.

• YouTube
• 24 minutes
• Self-Paced
• Free Video