Hyperparameter Optimization Courses and Certifications

Coursera

Fine-tuning Text Models with PEFT

Fine-tune large language models on consumer GPUs: implement QLoRA and LoRA, tune learning rates, batch sizes and rank, and evaluate with perplexity, BLEU, ROUGE and BERTScore.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Building, Optimizing, and Validating Machine Learning Models

Train and compare models with ML APIs, tune hyperparameters, validate with k-fold and stratified sampling, explain results with SHAP, and automate end-to-end ML pipelines.

• Coursera
• 9 weeks, 1 hour a week
• Self-Paced
• Paid Course

Packt

Applied Machine Learning and Model Optimization

Implement regression, classification, and ensemble algorithms such as XGBoost and CatBoost in Python, then optimize models with feature engineering, cross-validation, regularization, and GridSearchCV hyperparameter tuning.

• Coursera
• 7 weeks, 3 hours a week
• Self-Paced
• Paid Course

KodeKloud

ML Model Development and Tracking: Hands-on Guide

Track machine learning experiments with MLflow: log parameters and metrics, version artifacts through the Model Registry, compare CPU and GPU training, and automate insurance claim reviews.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

H2O.ai

H2O Hydrogen Torch Starter Course

Build deep learning models for computer vision, NLP, and audio without coding: import datasets, run experiments, and tune hyperparameters with grid search in Hydrogen Torch.

• Coursera
• 2 hours 17 minutes
• Self-Paced
• Paid Course

Coursera

Optimize Deep Learning Models for Peak AI

Fine-tune pretrained models with freezing strategies, then compare configurations on accuracy, latency, and memory using hyperparameter tuning and quantization to pick production-ready models.

• Coursera
• 1 hour 58 minutes
• Self-Paced
• Paid Course

Coursera

Optimize AI: Fine-Tune & Maximize Accuracy

Fine-tune a pre-trained ViT-B/16 model by freezing and unfreezing layers, and compare cosine decay and one-cycle learning-rate schedules using training curves.

• Coursera
• 1 hour 24 minutes
• Self-Paced
• Paid Course

Coursera

Engineer AI Models: Explain, Tune & Experiment

Move AI projects from black box to business-ready: apply feature engineering, hyperparameter tuning, SHAP and LIME explanations, and structured experiments to fraud and credit models.

• Coursera
• 2 hours 21 minutes
• Self-Paced
• Paid Course

Coursera

Automate, Optimize, and Monitor ML Models

Keep production models from decaying: evaluate deployed model performance, detect and remediate drift, and build automated end-to-end ML pipelines with hyperparameter optimization.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Deep Learning Model Engineering and Optimization

Select and justify architectures (MLP, CNN, Transformer), train baseline networks in PyTorch, then tune with dropout, L2 weight decay, Adam, gradient clipping and learning rate scheduling.

• Coursera
• 5 weeks, 3 hours a week
• Self-Paced
• Paid Course

Edureka

Getting Started with Automated Machine Learning (AutoML)

Build, evaluate and deploy models with H2O AutoML: interpret leaderboards, prevent data leakage, run grid searches with early stopping, and ship MOJO artifacts for batch scoring.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Board Infinity

Mastering Tabular ML: Feature Engineering to Production

Build a ride-hailing dynamic pricing engine: engineer 150+ features with Featuretools, master XGBoost and LightGBM internals, tune with Optuna, stack ensembles, and deploy via FastAPI.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Experiment Management, Tuning & Debugging

Build an automated, memory-efficient fine-tuning pipeline combining 4-bit foundation models, LoRA, MLflow tracking, and Azure ML sweeps with Bandit early stopping.

• Coursera
• 9 weeks, 1 hour a week
• Self-Paced
• Paid Course

Packt

Data Science - Supervised Machine Learning in Python

Apply supervised machine learning theory in Python through hands-on work with real datasets, model tuning, and deployment as web services.

• Coursera
• 8 weeks, 1 hour a week
• Self-Paced
• Paid Course

Career Certificate

Intermediate Machine Learning

Builds intermediate machine learning skills through advanced supervised models, hyperparameter tuning, ensemble methods, dimensionality reduction, and a portfolio project.

• Codecademy
• 8 hours
• Self-Paced
• Free Trial Available