Data and AI

Fine Tuning and Model Evaluation

Adapt a pretrained model to a specific task or style, and rigorously evaluate whether that adaptation actually worked

18h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can fine-tune a pretrained model for a specific task, choose appropriate fine-tuning techniques for your resource constraints, and design a genuine evaluation process that reveals whether fine-tuning actually improved the model rather than merely changed it.

Job titles this qualifies you for

Machine Learning EngineerAI EngineerLLM Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
When Fine-Tuning Is Genuinely the Right Approach
30 min
2
Preparing Data and Running a Fine-Tuning Job
40 min
Stage 2 — Applied
1
Evaluating Whether Fine-Tuning Actually Worked
35 min
2
Cost-Benefit Analysis and Responsible Deployment
25 min
Tier 2 Proof Submission

Fine Tuning and Model Evaluation Portfolio

Demonstrate genuine fine-tuning-versus-RAG judgement, data quality discipline, rigorous baseline evaluation, and cost/responsibility awareness.

1.A RAG-versus-fine-tuning scenario with reasoning
2.A low-quality data example and a data-volume explanation
3.A baseline-comparison explanation and a held-out-example limitation
4.A cost-not-justified scenario and a bias/representativeness explanation
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