Hyperparameter Tuning Courses and Certifications

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

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

63534 ratings at Coursera

Enhance deep learning skills: master hyperparameter tuning, regularization, optimization, and TensorFlow implementation for improved neural network performance and systematic results generation.

• Coursera
• 23 hours 35 minutes
• Self-Paced
• Paid Course

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

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

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

AutoML Seminars

Open Source Vizier: Distributed Infrastructure for Blackbox Optimization and Hyperparameter Tuning

Explore Google's Vizier system for blackbox optimization and hyperparameter tuning, covering its distributed infrastructure, Python implementation, and integration with AutoML ecosystem.

• YouTube
• 42 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

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

Hyperparameter Tuning in Python

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Hyperparameter Tuning in R

Learn how to tune your model's hyperparameters to get the best predictive results.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

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

Federated Hyperparameter Tuning - Challenges, Baselines, and Connections

Explores challenges and solutions for hyperparameter tuning in federated learning, introducing FedEx method to accelerate the process. Connects to weight-sharing in neural architecture search and demonstrates improved accuracy on benchmarks.

• YouTube
• 57 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