PH
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 55m | Size: 960 MB
The Most Practical Course on AI Evaluation You’ll Ever Come Across
What you'll learn
15,000+ Real Jailbreak Prompts and 50+ Benchmark Datasets
40+ Evaluation Tools (including Red Teaming) & 30+ Mitigation Techniques
Step by step guide for Red-Teaming with live demonstration using DeepTeam
5 Case Studies Covering- Fairness, Toxicity, Explainability, Privacy and Adversarial Testing
Requirements
Prerequisites for this course include a basic understanding of AI concepts and the AI development lifecycle. Prior hands-on experience is also recommended, especially for engaging effectively with case studies and conducting quantitative assessments..
Description
The AI Literacy Specialization Program is one-of-a-kind hierarchical & cognitive skills based curriculum that teaches artificial intelligence (AI) based on a scientific framework broken down into four levels of cognitive skills.Part 3: Analyze and Evaluate combines the below two cognitive skills -Analyzing (examining model's outputs to identify biases that may have been learned from the training data)Evaluating (assessing the performance, ethics, and overall effectiveness of an AI system)Whether you’re a policymaker, red teamer, LLM safety auditor, risk manager, AI developer, or a certification aspirant (AIGP, RAI, AAIA), this program gives you what theory alone cannot: applied, practice-ready evaluation and mitigation skills.The course is structured into four progressive modules: what to evaluate, how to evaluate, how to mitigate, and real-world case studies:IntroductionWhy Evaluate AI?Who Should Evaluate & When?What to Evaluate?1.1) Standard Safety Evaluation1.2) Frontier Capabilities & Misuse1.3) Misalignment1.4) Structural & Multi-Agent RisksHow to Evaluate?2.1) Overview2.2) Benchmarks & DatasetsBenchmark Repository v0.12.3) Evaluation Metrics2.4) Evaluation Techniques2.5) Red Teaming 101Evaluation Toolkit Repository v0.1How to Mitigate?3.1) How to MitigateMitigation Techniques Repository v0.1Case Studies4.1) Case Study 1: Automated Evaluation of LLMs Using Standard Eval Framework4.2) Case Study 2: Measuring Model Performance and Enhancing Explainability of a Credit Risk Model4.3) Case Study 3: Measuring Group Fairness in Housing Price Prediction4.4) Case Study 4: Measuring Toxicity and Using Red-Teaming in Resume Screening Tool4.5) Case Study 5: Identifying PII in RAG-Based Resume-to-JD MatcherYou’ll also find 50+ benchmark datasets (over 1.3 million data points), 40+ open-source tools, and a structured Advanced Red Teaming framework powered by 15,000+ jailbreak prompts. From identifying risks in standard safety scenarios and multi-agent systems to detecting fairness gaps in housing predictions or PII leaks in RAG pipelines, this course takes you from conceptual understanding to real-world auditing. This isn’t just another AI governance theory course; it is your operational playbook for evaluating and improving the next generation of generative and agentic systems.
Who this course is for
This course is designed for professionals working across the AI governance, risk, and compliance ecosystem. If you're an AI developer building generative or agentic systems, a policy maker or auditor navigating emerging AI regulations, a red teamer or LLM safety evaluator probing model behavior, or an alignment researcher or ethics/compliance executive shaping responsible AI strategies- this course is built for you. It’s also ideal for individuals stepping into the space through certifications like AIGP, RAI, or AAIA, and seeking to move from theoretical knowledge to hands-on evaluation and implementation.
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