Fine-Tuning Courses and Certifications

EDUCBA

Credit Default Prediction with Python: Apply & Analyze

Master credit default prediction using Python through logistic regression, decision trees, and ensemble methods with hands-on data preprocessing and model optimization techniques.

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

Introduction to Transformer Models for NLP: Unit 3

Explore advanced transformer models including T5 and Vision Transformers, fine-tune for NLP tasks, build image captioning systems, and deploy models using MLOps and FastAPI.

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

Whizlabs

Fundamentals of Machine Learning

Master machine learning fundamentals with hands-on experience in supervised/unsupervised learning, data preparation, model optimization, and cloud deployment on AWS and Azure.

• Coursera
• 12 hours 39 minutes
• Self-Paced
• Paid Course

Coursera

Databricks GenAI Engineering

Master building production-grade GenAI apps on Databricks by constructing RAG pipelines, fine-tuning models with MLflow, and evaluating responses for relevance, hallucination rate, and latency.

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

Optimizing Foundation Models

Explore RAG and fine-tuning techniques to enhance foundation model performance using AWS services, vector databases, and agents for multi-step tasks.

• Coursera
• 1 hour 3 minutes
• Self-Paced
• Paid Course

KodeKloud

Deploy ML Models to Production

Master MLOps by deploying ML models from development to production using MLflow, BentoML, and AWS SageMaker with hands-on insurance claim application project.

• Coursera
• 3 hours 53 minutes
• Self-Paced
• Paid Course

Starweaver

GenAI Model Development and Production Engineering

Master advanced fine-tuning techniques like LoRA, architect bulletproof deployment infrastructure, and build production GenAI systems that scale reliably to millions of users.

• Coursera
• 10 hours 38 minutes
• Self-Paced
• Paid Course

Coursera

Optimize Deep Learning: Tune PyTorch Models

Master PyTorch Lightning to implement custom callbacks, debug training issues, and optimize deep learning models for stable, production-ready performance.

• Coursera
• 3 hours 40 minutes
• Self-Paced
• Paid Course

Coursera

Build & Adapt LLM Models with Confidence

Master enterprise-grade LLM deployment with hands-on fine-tuning, architecture selection, and production scaling using LoRA, Hugging Face, and security best practices.

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

Packt

Applied Machine Learning and Model Optimization

Master advanced ML algorithms, hyperparameter tuning, and model optimization techniques through hands-on projects with XGBoost, ensemble methods, and real-world applications.

• Coursera
• 21 hours 9 minutes
• Self-Paced
• Paid Course

H2O.ai

H2O Hydrogen Torch Starter Course

Master deep learning without coding - build computer vision, NLP, and audio models using H2O Hydrogen Torch. Learn dataset handling, model tuning, and real-world applications through hands-on experimentation.

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

KodeKloud

ML Model Development and Tracking: Hands-on Guide

Master MLOps "Middle Loop" with MLflow tracking, model versioning, and production deployment through hands-on insurance claim automation project.

• Coursera
• 3 hours 3 minutes
• Self-Paced
• Paid Course

Packt Career Certificate

LLMs in Enterprise

Explore the full lifecycle of LLMs in enterprise settings, from foundational concepts to advanced fine-tuning, deployment, and responsible AI practices for building production-grade solutions.

• Coursera
• Self-Paced
• Paid Course
• 3 courses

Coursera

Automate, Evaluate and Deploy ML Models Confidently

Master production-grade MLOps by building automated CI/CD pipelines with GitHub Actions, analyzing Optuna trials, and implementing validation gates for confident model deployment.

• Coursera
• 2 hours 49 minutes
• Self-Paced
• Paid Course

Packt

Building LLM Powered Applications

Explore how to build LLM-powered applications using LangChain, prompt engineering, and multimodal AI, covering structured data, fine-tuning, and responsible AI practices.

• Coursera
• 19 hours 57 minutes
• Self-Paced
• Paid Course