All Episodes
Displaying 1 - 20 of 50 in total
Episode 1 — Orientation: How to Learn AI by Listening
This opening episode sets the foundation for the entire PrepCast by guiding learners on how to approach the subject of artificial intelligence in an audio-first format...
Episode 2 — What Is AI? Definitions, Scope, Everyday Uses
This episode introduces the learner to the essential definitions and scope of artificial intelligence, a foundational step in any exam or certification path. AI can me...
Episode 3 — A Short History of AI: Booms, Winters, Breakthroughs
This episode provides context for the development of artificial intelligence by tracing its history across cycles of optimism, disappointment, and eventual breakthroug...
Episode 4 — How AI Systems Work: Data, Models, Feedback Loops
This episode introduces the structural mechanics of AI systems, breaking them into three interrelated components: data, models, and feedback loops. Data is the raw mat...
Episode 5 — Glossary Deep Dive I: Core Terms You’ll Hear Often
This episode serves as a glossary immersion, focusing on the terminology that certification candidates will encounter repeatedly in AI-related exams. Terms like algori...
Episode 6 — Types of AI: Narrow vs. General, Symbolic vs. Statistical
This episode examines the main types of artificial intelligence, clarifying distinctions that are essential for both exams and real-world comprehension. Narrow AI, als...
Episode 7 — Problem Framing: Turning Goals into AI Questions
This episode introduces problem framing, the skill of converting a business or operational goal into a question that an AI system can realistically address. For certif...
Episode 8 — Data for AI: Collection, Labeling, and Quality Basics
This episode explores the critical role of data in artificial intelligence, focusing on collection, labeling, and quality considerations. Data is the foundation of any...
Episode 9 — Data Bias Preview: Sources, Signals, Mitigations
This episode introduces the concept of data bias, a topic that often appears in certification exams because of its impact on fairness, accuracy, and compliance. Bias a...
Episode 10 — ML 101: Supervised Learning in Plain Language
This episode explains supervised learning, one of the most fundamental approaches in machine learning and a cornerstone for certification exams. Supervised learning re...
Episode 11 — ML 102: Unsupervised Learning and Clustering
This episode introduces unsupervised learning, a key machine learning paradigm that does not rely on labeled data. Instead of mapping known inputs to known outputs, un...
Episode 12 — ML 103: Reinforcement Learning at a High Level
This episode introduces reinforcement learning, often considered the third major paradigm of machine learning. Unlike supervised and unsupervised learning, reinforceme...
Episode 13 — Evaluating Models: Accuracy, Precision/Recall, AUC
This episode addresses model evaluation, a core competency for certification exams. While accuracy is the simplest metric, it is not always sufficient, especially when...
Episode 14 — Overfitting & Generalization: When Models Fool You
This episode explains overfitting, one of the most important pitfalls in machine learning. Overfitting occurs when a model memorizes training data so closely that it f...
Episode 15 — Feature Engineering: From Raw Data to Signals
This episode introduces feature engineering, the process of transforming raw data into meaningful inputs that improve model performance. Features are the variables the...
Episode 16 — From Rules to Learning: Why ML Beat Expert Systems
This episode reviews the transition from expert systems, which dominated AI development in the 1970s and 1980s, to the rise of machine learning approaches that define ...
Episode 17 — Deep Learning Basics: Neurons, Layers, Training Intuition
This episode introduces deep learning, a subset of machine learning that relies on neural networks with many layers to learn complex representations of data. At its co...
Episode 18 — Computer Vision Basics: From Pixels to Patterns
This episode explores computer vision, the field of AI that enables systems to interpret and analyze visual data. At the most basic level, digital images are arrays of...
Episode 19 — Speech & Audio AI: STT, TTS, and Speaker ID
This episode introduces the fundamentals of speech and audio AI, covering three main areas: speech-to-text (STT), text-to-speech (TTS), and speaker identification. STT...
Episode 20 — NLP Foundations: Pre-LLM Techniques Explained
This episode covers the foundations of natural language processing (NLP) before the rise of large language models. Early NLP techniques relied heavily on statistical a...