Certified - Advanced AI Audio Course

By Jason Edwards

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Episodes: 51

Description

The Advanced Artificial Intelligence Audio Course is a focused, audio-first series that takes you deep into the technical foundations and emerging challenges of modern AI systems. Designed for professionals, students, and certification candidates, this course explains advanced AI concepts through clear, structured narration—no slides, no filler, just direct, practical learning. Each episode unpacks core topics such as neural architectures, model embeddings, optimization, interpretability, and evaluation, showing how these elements come together to create powerful and reliable AI systems. Whether you’re working in development, research, or applied security, the course helps you understand how modern models are designed, trained, and deployed in real-world environments. Beyond architecture and algorithms, this Audio Course also explores the resilience and trustworthiness of AI—examining attack surfaces, data poisoning, model inversion, and the security controls needed to protect AI systems throughout their lifecycle. It provides insight into ethical risks, bias mitigation, governance frameworks, and assurance practices that keep advanced models safe and compliant. You’ll learn how leading organizations balance innovation with reliability, and how these same principles can guide your own technical and professional growth. Developed by BareMetalCyber.com, the Advanced Artificial Intelligence Audio Course delivers in-depth, exam-aligned instruction that bridges theory with practical application. Each episode builds technical fluency while reinforcing best practices in AI design, operations, and governance—helping you think critically, work securely, and lead confidently in the evolving world of intelligent systems.

Episode Date
Welcome to the Intermediate AI Audio Course
Oct 14, 2025
Episode 50 — Optimization & Decision Intelligence: Linear Programming, Constraints, and Trade-Offs
Sep 14, 2025
Episode 49 — Causal Inference for Practitioners: Experiments, A/B Tests, and Uplift
Sep 14, 2025
Episode 48 — Time Series & Forecasting: Trends, Seasonality, and Drift
Sep 14, 2025
Episode 47 — Recommender Systems: Ranking, Diversity, and Feedback Loops
Sep 14, 2025
Episode 46 — Working with Vendors: Questions to Ask, SLAs to Watch
Sep 14, 2025
Episode 45 — Building with Ethics: Practical Guardrails for Projects
Sep 14, 2025
Episode 44 — Agents & Tool Use: When Models Act on Your Behalf
Sep 14, 2025
Episode 43 — Edge & On-Device AI: Privacy, Latency, Offline Use
Sep 14, 2025
Episode 42 — AI in Healthcare & Finance: Safety-Critical Considerations
Sep 14, 2025
Episode 41 — AI in Cybersecurity: Detection, Triage, Automation
Sep 14, 2025
Episode 40 — AI in Operations & IT: Forecasting and Anomaly Detection
Sep 14, 2025
Episode 39 — AI in Marketing & Sales: Personalization and Scoring
Sep 14, 2025
Episode 38 — AI in Customer Support: Chatbots, Agents, Escalations
Sep 14, 2025
Episode 37 — Organizational Roles: Who Does What on an AI Team
Sep 14, 2025
Episode 36 — Change Management: Helping Teams Adopt AI
Sep 14, 2025
Episode 35 — Metrics That Matter: Measuring Value, Not Hype
Sep 14, 2025
Episode 34 — Legal & Policy Landscape: Copyright, Consent, Compliance
Sep 14, 2025
Episode 33 — AI Security Primer: Threats and Defenses
Sep 14, 2025
Episode 32 — Data Privacy & Governance: Responsible Data Use
Sep 14, 2025
Episode 31 — MLOps Essentials: Monitoring, Drift, and Lifecycle
Sep 14, 2025
Episode 30 — Productizing AI: From Prototype to Production (No Code)
Sep 14, 2025
Episode 29 — Human-in-the-Loop: People + AI for Better Outcomes
Sep 14, 2025
Episode 28 — Explainability & Transparency: Opening the Black Box
Sep 14, 2025
Episode 27 — Safety, Bias, and Fairness: What Can Go Wrong and Why
Sep 14, 2025
Episode 26 — Generative AI Beyond Text: Images, Audio, Video
Sep 14, 2025
Episode 25 — Embeddings & Vector Databases: Meaning as Numbers
Sep 14, 2025
Episode 24 — Retrieval-Augmented Generation (RAG): Using Your Own Data
Sep 14, 2025
Episode 23 — Prompting Fundamentals: Reliable Patterns and Pitfalls
Sep 14, 2025
Episode 22 — Large Language Models: What They Can and Can’t Do
Sep 14, 2025
Episode 21 — Transformers Explained: Attention Without Equations
Sep 14, 2025
Episode 20 — NLP Foundations: Pre-LLM Techniques Explained
Sep 14, 2025
Episode 19 — Speech & Audio AI: STT, TTS, and Speaker ID
Sep 14, 2025
Episode 18 — Computer Vision Basics: From Pixels to Patterns
Sep 14, 2025
Episode 17 — Deep Learning Basics: Neurons, Layers, Training Intuition
Sep 14, 2025
Episode 16 — From Rules to Learning: Why ML Beat Expert Systems
Sep 14, 2025
Episode 15 — Feature Engineering: From Raw Data to Signals
Sep 14, 2025
Episode 14 — Overfitting & Generalization: When Models Fool You
Sep 14, 2025
Episode 13 — Evaluating Models: Accuracy, Precision/Recall, AUC
Sep 14, 2025
Episode 12 — ML 103: Reinforcement Learning at a High Level
Sep 14, 2025
Episode 11 — ML 102: Unsupervised Learning and Clustering
Sep 14, 2025
Episode 10 — ML 101: Supervised Learning in Plain Language
Sep 14, 2025
Episode 9 — Data Bias Preview: Sources, Signals, Mitigations
Sep 14, 2025
Episode 8 — Data for AI: Collection, Labeling, and Quality Basics
Sep 14, 2025
Episode 7 — Problem Framing: Turning Goals into AI Questions
Sep 14, 2025
Episode 6 — Types of AI: Narrow vs. General, Symbolic vs. Statistical
Sep 14, 2025
Episode 5 — Glossary Deep Dive I: Core Terms You’ll Hear Often
Sep 14, 2025
Episode 4 — How AI Systems Work: Data, Models, Feedback Loops
Sep 14, 2025
Episode 3 — A Short History of AI: Booms, Winters, Breakthroughs
Sep 14, 2025
Episode 2 — What Is AI? Definitions, Scope, Everyday Uses
Sep 14, 2025
Episode 1 — Orientation: How to Learn AI by Listening
Sep 14, 2025