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Article Series · 10 published articles

Artificial intelligence

A practical journey from AI fundamentals to building real-world AI systems. The series begins with the basics of artificial intelligence, Python, data, and essential math, then gradually moves into machine learning, deep learning, computer vision, NLP, large language models, RAG, deployment, MLOps, and responsible AI. Each article focuses on clear explanations, real examples, and hands-on practice, with the goal of helping readers move from simply understanding AI concepts to actually building and working with AI systems.

Reading order

Read in order.

  1. What Is Artificial Intelligence? Artificial intelligence, or AI, is one of those terms people hear everywhere, but it can sound more complicated than it really is.
  2. AI, Machine Learning, Deep Learning, and Generative AI Artificial intelligence can sound like a giant robot brain hiding behind a glowing screen. People talk about AI, machine learning, deep learning, neural networks, generative AI, large language models, computer vision, and natural language processing as if they are all the same thing.
  3. How Machines Learn From Data Computers are often described as machines that follow instructions. In traditional programming, a developer writes rules that tell a computer exactly what to do. For example, a programmer might create a rule stating that an email should be marked as spam when it contains certain suspicious words.
  4. Features, Labels, Inputs, and Outputs Can we estimate what a house will sell for? Can we tell whether a customer is likely to cancel a subscription? Can we identify a defective product before it leaves the factory?
  5. The Complete AI Development Pipeline: From Idea to Deployed AI System Artificial intelligence can sometimes look like magic. You give a computer thousands of examples, train a model, and suddenly it can recognize spam emails, detect defects in products, analyze medical images, or answer questions about documents.
  6. What Makes a Good AI Problem? Artificial intelligence is powerful, but that does not mean every problem should be solved with AI. In many situations, a simple rule, a spreadsheet formula, or basic automation can solve the problem faster, more cheaply, and more reliably. A good AI project begins by asking a more important question than “How can we use AI?” The better question is: “Does this problem actually need AI?”
  7. Python for AI: The Minimum You Actually Need If you already know how to write a variable, create a function, run a loop, and use an `if` statement, you know enough Python to start moving toward AI.
  8. Working With Data Using Pandas Once you move from basic Python into data science, machine learning, or AI, one of the first things you need to become comfortable with is working with tables of data. Most AI projects do not begin with a clever model or a complicated algorithm.
  9. Numbers, Arrays, and NumPy: How AI Represents Data This article explains how machine-learning systems represent real-world information as numerical data. It introduces NumPy arrays and shows how they differ from regular Python lists, then builds the foundation for understanding scalars, vectors, matrices, dimensions, and shapes. Through practical examples, it shows how students, images, text, and other real-world data can be converted into numerical structures that AI models can process efficiently.
  10. The Math You Actually Need for AI The article introduces the core mathematics needed to begin machine learning without overwhelming the reader with advanced theory. It uses a simple student-score prediction example to explain mean, standard deviation, probability, linear relationships, functions, prediction error, vectors, and matrices. The central idea is that AI math becomes much easier when each concept is tied to a practical purpose. The article shows how data is summarized, how relationships are identified, how models turn inputs into outputs, and how prediction errors can be measured using Mean Absolute Error. It also reinforces that libraries such as NumPy handle the calculations, while the reader’s job is to understand what those calculations mean. The article ends by preparing the reader for the next step: training a real machine-learning model.