Certified - Introduction to AI Audio Course

AI is not confined to the cloud — it increasingly lives in the devices around us. This episode introduces edge AI, where models run locally on Internet of Things (IoT) devices. Benefits include lower latency, improved privacy, and functionality even without network connections. We’ll explain how embedded machine learning models are compressed and optimized for limited hardware, and how techniques...

Not every organization can build AI systems from scratch, and cloud AI services fill this gap by offering ready-made tools. This episode explains how major providers such as Amazon Web Services, Microsoft Azure, and Google Cloud deliver APIs for natural language processing, vision, and speech. Pre-trained models allow companies to adopt AI quickly, while platforms like SageMaker and Vertex AI offe...

For AI to succeed, people must be able to use it effectively. This episode examines the design of interfaces that allow humans to interact with AI in ways that are intuitive, transparent, and supportive of trust. We start with dashboards, conversational agents, and voice interfaces, then explore adaptive systems that personalize recommendations and adjust to user behavior. Transparency features, s...

AI systems are only as good as the data and assumptions that shape them, and many fail because of recurring pitfalls. This episode outlines the most common problems, starting with poor data quality, unbalanced datasets, and labeling errors. We’ll discuss sampling bias, measurement bias, and the use of proxy variables that inadvertently encode sensitive traits. Overfitting, underfitting, and automa...

Knowing that an AI model works is not enough — we need to know how well it works, and under what conditions. This episode explores the frameworks and metrics used to evaluate AI performance. We begin with accuracy, precision, recall, F1 score, and confusion matrices for classification problems, then move to regression metrics like mean squared error and R². For clustering and ranking tasks, we cov...

Once data is prepared, models must be built and evaluated with rigor. This episode covers the three pillars of evaluation: training, validation, and testing. Training introduces the algorithm to data, refining weights and parameters over multiple epochs. Validation checks progress midstream, guiding hyperparameter tuning and preventing overfitting. Testing provides the final check, using unseen da...

Data is not just fuel for AI; it must be carefully gathered, cleaned, and prepared to produce reliable results. This episode breaks down the full lifecycle of data preparation, from collection through preprocessing. You’ll hear about structured, semi-structured, and unstructured data, and the importance of cleaning, labeling, and augmenting datasets. Normalization, handling missing values, and fea...

While much of AI lives in code and data, robotics brings intelligence into the physical world. This episode examines how robots integrate sensing, reasoning, and action. We begin with perception technologies such as cameras, lidar, and tactile sensors, followed by motion planning, control systems, and kinematic models that enable movement. Manipulation, navigation, and localization are explained a...

Speech is one of the most natural ways humans communicate, and AI systems are increasingly able to listen and respond. This episode covers speech recognition, the conversion of audio into text, and speech generation, the production of lifelike voice outputs. We trace the path from early statistical methods like hidden Markov models to deep learning architectures that now dominate. You’ll learn abo...

The ability to process visual information has been a defining achievement for AI. In this episode, we explore how computer vision allows machines to interpret and analyze images and video. We start with early techniques like edge detection and feature extraction, then move into modern convolutional neural networks that revolutionized accuracy in object detection and classification. Segmentation, o...

Language is one of the most human forms of intelligence, and this episode explores how AI systems learn to read, interpret, and generate text. We begin with early approaches like rule-based translation, then move into statistical models such as bag-of-words and word embeddings. Tokenization, part-of-speech tagging, syntax parsing, and semantic analysis are explained as core steps in processing hum...

Deep learning represents the cutting edge of neural networks, pushing performance far beyond earlier methods. In this episode, we define deep learning as networks with many layers capable of learning hierarchical features, supported by massive datasets and specialized hardware like GPUs. We’ll explore architectures including convolutional neural networks for vision, recurrent and gated networks fo...

Artificial neural networks are inspired by the structure of the human brain but simplified into mathematical models that drive today’s most powerful AI systems. In this episode, we begin with the perceptron, an early model of a single artificial neuron, then explore how weights, activation functions, and layers combine to process information. Multi-layer networks, trained through backpropagation a...

Machine learning is the beating heart of modern AI, and this episode introduces its three foundational approaches: supervised, unsupervised, and reinforcement learning. We begin with supervised learning, where labeled data pairs inputs with correct outputs, powering tasks like classification and regression. We then shift to unsupervised learning, where algorithms find hidden structure in unlabeled...

Real-world decisions are rarely black and white, and AI systems must navigate uncertainty just as humans do. This episode explores how probability theory underpins reasoning when outcomes are incomplete, noisy, or ambiguous. We begin with core concepts like random variables, probability distributions, and conditional probability, then move to Bayes’ theorem as a method for updating beliefs with ne...

Reasoning has always been at the heart of intelligence, and in this episode we focus on how AI systems use logic to derive conclusions. Starting with propositional and predicate logic, we’ll explain how knowledge can be structured into true or false statements and rules. Deductive, inductive, and abductive reasoning are compared as different ways to reach conclusions from data or hypotheses. You’l...

For AI to reason, it needs to store and organize information. This episode explores knowledge representation, the frameworks that allow machines to capture facts, relationships, and rules. From semantic networks linking concepts to ontologies defining categories, we examine how different structures model the world. Logic-based systems like first-order logic provide precision, while production rule...

Before machine learning took center stage, AI was already grappling with how to solve problems systematically. This episode dives into search and problem solving, two of the earliest and still fundamental approaches to intelligence. You’ll learn how problems are represented as states and transitions, and how uninformed search strategies like breadth-first and depth-first explore possibilities blin...

No matter how advanced the algorithm, it can’t run without data. This episode focuses on why data is considered the fuel of AI, exploring the different types that drive training and performance. Structured data, such as rows in databases, is contrasted with unstructured data like images, text, and audio. We’ll examine the steps needed to prepare data — collecting, cleaning, labeling, and augmentin...

When people talk about machines “thinking,” they’re not talking about human intuition or creativity. They’re talking about algorithms — structured sets of instructions — and representations, the ways information is stored and processed. In this episode, we look at how computers encode numbers, words, and images, and how those encodings become the raw material for reasoning. You’ll learn about symb...

AI, machine learning, and deep learning are terms often used interchangeably, but they are not the same — and confusing them makes it harder to understand the field. This episode clears the fog by breaking down how these layers of terminology connect. We’ll begin with Artificial Intelligence as the broadest category: any system designed to mimic aspects of human thought. Within that sits machine l...

Artificial Intelligence didn’t appear overnight; it has a story stretching back more than seven decades. In this episode, we step into that story, beginning with Alan Turing’s famous question — can machines think? — and the Turing Test that followed as an early benchmark for intelligence. We’ll visit the 1956 Dartmouth Conference where the term “Artificial Intelligence” was first coined, and hear...

This PrepCast is designed to teach Artificial Intelligence in a way that fits into real life: no slides, no diagrams, no heavy math on the page — just clear explanations you can absorb anywhere. In this roadmap episode, we walk through the design of the series, showing how the episodes are structured so you can either listen sequentially and build a complete foundation or drop into individual topi...

Artificial Intelligence is a term everyone has heard, but few understand in depth. In this opening episode, we cut through the hype and get to the core: what does it actually mean when we say a system is “intelligent”? You’ll hear how the idea of machines that mimic human thought emerged, why early approaches like rule-based programming fell short, and how modern data-driven methods reshaped the f...