Hyperparameter Tuning Courses and Certifications

Hyperparameter Tuning in Python

12 ratings at DataCamp

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

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

MathWorks

Predictive Modeling and Machine Learning with MATLAB

89 ratings

Learn to build, evaluate, and optimize regression and classification models using MATLAB. Apply supervised machine learning workflows to real-world datasets and improve model performance through feature selection and hyperparameter tuning.

• Coursera
• 18 hours 52 minutes
• Self-Paced
• Paid Course

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

63522 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

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

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

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

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