Certified AI Fine-Tuning & Reinforcement Learning Expert

• Prepare tuning datasets with train, validation, safety, and regression splits
• Compare LoRA, RLHF, DPO, and reward-model approaches for the target behavior
• Ship adapted models with registry, canary, and rollback controls

ML engineers and AI teams responsible for tuning, preference data, reward models, and adapted model releases.

• Dataset Preparation for Fine-Tuning

  Adaptation Foundations

• LoRA and Parameter-Efficient Tuning

  Adaptation Foundations

• RLHF Workflow

  Preference Optimization

• DPO and Preference Data

  Preference Optimization

• Experiment Tracking and Model Registry

  Training Operations

• Hands-on capstone: Plan a fine-tuning program with baseline evals, LoRA/DPO choices, reward-model risks, and model registry controls.
• Final quiz checks understanding across every module.
• Public credential ID makes the result easy to verify.