Artificial Intelligence (AI) Foundations for Developers

This lecture provides a clear conceptual overview of what artificial intelligence truly means. It defines AI as intelligent behavior through computation, explains that AI is a broad family of approaches rather than a single technology, and emphasizes the developer’s need to think beyond coding to understand AI systems. The session sets a strong foundation for exploring the drivers and impact of the AI revolution in upcoming lectures.

Explore the key forces fueling the AI revolution—data, compute, and algorithms—and how they’ve transformed AI from academic theory to industrial reality. Understand why AI is booming now and what it means for developers stepping into this new era.

This lecture explores how AI is fundamentally transforming the software development lifecycle. It highlights the shift from manual coding to intelligent collaboration, showcasing how AI augments coding, testing, debugging, code review, and documentation. Developers are evolving from code writers to system orchestrators, leveraging AI to enhance productivity and creativity. The session also introduces emerging career paths in the AI-driven landscape, emphasizing the new mental models needed to thrive alongside AI.

This lecture, "Navigating the AI Landscape: User, Integrator, Builder," provides a clear conceptual map for developers engaging with AI. We'll delineate three key roles: the AI User, who leverages off-the-shelf AI tools; the AI Integrator, who embeds AI APIs and pre-built models into existing software; and the AI Builder, who trains and deploys custom AI models. We'll explore the distinct responsibilities, essential conceptual skill sets, and common career paths for each role, highlighting where they overlap, all while maintaining a theoretical focus without diving into specific tool details.

This lecture explores the conceptual journey data takes before it ever reaches an AI model. From acquisition and storage to cleaning, profiling, and transformation, we break down the key stages that shape data readiness. Emphasizing the principle of “Garbage In, Garbage Out,” developers will learn to treat data pipelines with the same care as software architecture, understanding how early decisions directly impact model performance.

This lecture explores how AI systems learn through structured data inputs, focusing on the conceptual roles of features (inputs) and labels (desired outputs). It unpacks why data must be transformed into meaningful, model-friendly representations, and how choices in data design shape what a model can understand. Developers will gain a deeper understanding of how representation, not just raw data, defines the boundaries of machine learning—and why thoughtful data design is the first step toward building intelligent systems.

This lecture introduces the core machine learning paradigms—supervised, unsupervised, and reinforcement learning—as distinct ways of framing problems. It emphasizes that paradigm choice is a design decision driven by task type, data availability, and product context. Learners explore how each approach learns differently and when to apply them, cultivating a problem-first mindset essential for effective ML development.

This lecture introduces core concepts and vocabulary essential for understanding machine learning. It explains what models are, how datasets support learning, and the processes of training and inference. Key challenges like overfitting, underfitting, bias, and variance are defined conceptually to build a solid mental framework. By grasping these terms, developers gain clarity on how AI systems learn, generalize, and make predictions — laying the foundation for deeper study.

This lecture introduces neural networks as the foundation of modern AI. It explains how they learn from data, make predictions, and improve through feedback. You’ll explore key concepts like neurons, layers, weights, and why “deep” learning involves multiple layers of abstraction. The session also covers common types of neural networks and real-world use cases, laying the groundwork for understanding how AI systems learn and evolve.

This lecture explores how AI systems convert raw data—especially language—into meaningful numerical forms they can understand. You’ll learn how tokenization breaks text into manageable pieces, how embeddings transform those pieces into rich numeric representations capturing meaning, and how vector spaces organize these embeddings to reveal relationships. Together, these concepts form the foundation that enables AI to interpret, compare, and generate language effectively.

This lecture explores why accuracy alone can be misleading when evaluating machine learning models. You'll learn about different types of errors (false positives/negatives), key metrics like precision, recall, and F1-score, and how to choose the right metric based on your problem’s real-world impact. The goal is to help you think critically about what “good performance” really means.

This lecture introduces LLMs as advanced text prediction engines that generate language by predicting one token at a time. It covers the foundational Transformer architecture concepts like self-attention and positional encoding, explains the vast and diverse training data behind LLMs, and highlights the model’s limited “context window.” The goal is to build a clear mental model of how LLMs work conceptually, without technical complexity.

This lecture dives into the core text generation process of LLMs, focusing on the probabilistic next-token prediction loop. It explains key concepts like sampling, temperature, Top-K, and Top-P, showing how these parameters influence creativity, randomness, and coherence. It emphasizes how developers steer output through both prompts and parameter settings, balancing control and creativity to guide LLM-generated content.

This lecture introduces Prompt Engineering Principles — the art of crafting prompts to guide large language models effectively. It covers key prompting techniques like zero-shot, few-shot, chain-of-thought, role prompting, and prompt chaining. You’ll learn how small changes in phrasing impact model behavior, how abstraction levels affect flexibility, and how to think of prompts as interfaces that shape AI reasoning. The session builds a mindset of strategic communication — not just asking questions, but designing context and guiding thought.

This lecture explores the core limitations of Large Language Models — from hallucinations and bias to context size and cost. You'll learn why these aren’t just flaws, but design trade-offs, and how understanding them helps you build smarter, more reliable AI systems.

In this lecture, we explore two powerful strategies to customize how Large Language Models behave and what they know — Fine-tuning and Retrieval-Augmented Generation (RAG). You'll learn the core mental models behind each approach: fine-tuning embeds knowledge into the model, while RAG retrieves it on demand from external sources.

We’ll walk through use cases, trade-offs in cost, speed, and flexibility, and how to decide which method fits your needs — or whether you should combine both. This session is about thinking like a system designer: not just choosing tools, but choosing the right approach for your data, product, and goals.

No code. No math. Just clear thinking.

This lecture introduces GitHub Copilot as a new kind of AI-powered coding assistant. You’ll learn what Copilot really is, how it works inside your editor, and why it's more than just autocomplete. It’s not about replacing developers — it’s about reducing friction, accelerating routine tasks, and giving you a smarter pair programmer on demand.

This lecture explores how AI-native IDEs and tools like ChatGPT are transforming the development workflow. From intelligent debugging to smart navigation and contextual assistance, you'll learn how these assistants enhance clarity, reduce friction, and act as collaborative partners in your coding process—not just tools.

Discover how AI can transform the way developers approach testing and documentation. This lecture explores how AI helps generate smarter test cases, create realistic test data, and automate documentation drafting—making quality assurance faster, easier, and more thorough without replacing human judgment. Learn how AI acts as a powerful assistant to boost your productivity and code quality.

This lecture explores how AI enhances code quality by detecting bugs, spotting security flaws, and assisting in code reviews. It shows how AI works alongside developers to improve speed, accuracy, and software reliability—serving as a powerful partner, not a replacement.

This lecture explores how effective prompting is less about clever phrasing and more about structured thinking. You'll learn to break problems into clear parts, guide the AI with context and constraints, and improve results through iteration. We’ll also cover how your own mental model directly shapes prompt quality—turning prompting into a powerful extension of your thinking process.

This lecture gives a high-level overview of today’s top AI API platforms—like OpenAI, Google, AWS, and Hugging Face—highlighting what each excels at and how they differ. You'll learn how to compare them based on use case, modality (text, vision, speech), and ecosystem fit, with guidance on pricing models and trade-offs. It’s your starting point for choosing the right AI tools for real-world projects.

This lecture explores how AI APIs can power intelligent features in your applications — from text summarization to building simple chatbots. You'll learn how to integrate AI as a "brain" in your system, while you stay in control of the user experience. A practical foundation for adding meaningful intelligence to everyday software.

This lecture introduces semantic search, a powerful evolution beyond traditional keyword search. You’ll learn how AI transforms words into numeric embeddings that capture meaning, and how vector stores enable fast retrieval based on semantic similarity. Through clear conceptual explanations, we explore why semantic search feels more human, how it powers advanced AI like Retrieval-Augmented Generation (RAG), and why it’s foundational for smarter, context-aware applications.

This lecture explores the realities of integrating AI APIs into your systems—from understanding hidden costs and handling failures to designing resilient architectures. You'll learn why thoughtful design choices like retries, caching, and cost control are essential, and why treating AI as a strategic resource—not a magic solution—is key to building reliable, scalable AI-powered applications.

This lecture introduces AI agents — systems that pursue goals using a continuous loop of observation, reasoning, action, and reflection. You’ll learn how agents differ from traditional AI tools, explore their key components (like planning, memory, and tool use), and see real-world examples like AutoGPT and Devin. By the end, you’ll understand why agents represent a shift from using AI to building around it.

This lecture explores the practical realities of working with AI agents as a developer. You'll learn why agents require human oversight, how “autonomous” systems can fail without guidance, and what it really means to stay in the loop. From trust and accountability to the illusion of progress, we break down the critical role developers play in turning agent potential into real, reliable outcomes.

This lecture breaks down the core concepts of AI deployment, showing how the Model Control Plane (MCP) directs workloads and how to choose between local, cloud, and edge setups. You’ll learn the trade-offs between speed, privacy, and scalability, why hardware sets the limits, and how deployment choices shape real-world AI performance.

This lecture introduces the core ideas behind LLMOps—how to keep AI models healthy, relevant, and efficient throughout their lifecycle. We’ll explore why monitoring is essential to prevent silent performance drops, how scaling keeps AI cost-effective under growing demand, and how versioning ensures reproducibility and trust. The goal is to help you see AI not as a static deliverable, but as a living system that requires ongoing care and adaptation

This lecture explored essential security and privacy safeguards for AI systems, from defending against prompt injection attacks to ensuring data minimization and purpose limitation. We covered how anonymization and pseudonymization help protect identities while keeping data useful, and why secure API practices with encryption form a critical shield for AI operations. Together, these measures create a layered defense that keeps AI both safe and compliant.

This lecture explores the fundamental ethical challenges in AI, focusing on how bias originates from data, algorithms, and societal influences. It introduces core fairness concepts, the importance of transparency, and the role of human values in building trustworthy AI systems. Designed for developers new to AI, it emphasizes understanding and addressing bias to create fair and responsible technology.

This lecture introduces the essential concepts of AI safety, alignment, and responsible use. It explains why building trustworthy AI matters, highlights the importance of human oversight, and covers practical safeguards like input filtering and output moderation. The session also discusses accountability and governance frameworks that ensure ethical AI development. Designed for developers new to AI, it lays a solid foundation for creating AI systems that are safe, aligned with human values, and beneficial to society.

This lecture introduces how AI evolved from single-task systems to today’s multimodal models that understand and connect text, images, audio, and more — and explores the crucial role of specialized hardware like GPUs, TPUs, and NPUs in making these advanced capabilities possible.

This lecture introduces the evolving landscape of AI regulation and standards worldwide. It explains why rules and governance matter—not just as legal requirements but as essential foundations for building trustworthy, ethical AI. Developers will gain a clear understanding of global approaches, key compliance concepts like data governance and auditing, and how these shape responsible AI development. By the end, learners will appreciate their crucial role in creating AI that is both innovative and safe for society.