AI vs ML vs Deep Learning: Core AIF-C01 Concepts and Terminology

These five terms are not competing labels; they nest inside one another, and the exam expects you to know the ordering. Artificial intelligence (AI) is the widest circle: any technique that lets a computer mimic human intelligence, including old-fashioned rule-based systems that follow hand-written logic. Machine learning (ML) sits inside AI. It is the family of methods that learn patterns from data rather than being explicitly programmed with rules. Deep learning sits inside ML. It uses neural networks with many stacked layers to learn from large amounts of data, and it powers most modern breakthroughs in images and language. Generative AI (GenAI) is built on deep learning. It uses very large models (foundation models and large language models) to generate new content such as text, images, or code.

Agentic AI is the newest term. An agentic system uses a generative model as a reasoning engine, then adds memory and the ability to call tools so it can plan and take multi-step actions toward a goal, not just answer a single prompt. Think of it as GenAI that acts. The table below fixes the hierarchy in one view.

Four foundational words appear in almost every AIF-C01 question about how machine learning works, and distractors love to swap them. An algorithm is the method or procedure used to learn from data, for example a decision-tree or gradient-descent procedure. A model is the result you get after running that algorithm over data: the trained artifact that stores the learned patterns and can make predictions. In short, the algorithm is the recipe and the model is the cake.

Training is the phase where the algorithm processes example data and adjusts the model until it captures useful patterns. It is compute-heavy and usually happens before the model is used. Inferencing (also called inference) is the phase where the finished model is given new, unseen input and produces a prediction or output. Training builds the model; inferencing uses it. A helpful test for the exam: if the sentence describes the system learning from labeled or historical examples, it is training. If it describes the system answering a live request or scoring new records, it is inferencing. You pay for training once (or occasionally, when you retrain) but for inferencing every time the model is called, which is why the two are budgeted and scaled separately.

These terms name the technologies and problem areas the exam references repeatedly. A neural network is a model loosely inspired by the brain: layers of connected nodes that pass signals forward, with each connection weighted so the network can learn complex relationships. A neural network with many hidden layers is what makes something deep learning.

Computer vision is the field concerned with helping machines interpret images and video, such as detecting objects, reading faces, or spotting defects on a production line. Natural language processing (NLP) is the field concerned with understanding and working with human language, covering tasks like sentiment analysis, translation, and entity extraction. Speech recognition, which converts spoken audio into text, is closely related and often grouped with NLP applications.

A large language model (LLM) is a very large neural network trained on huge volumes of text so it can predict and generate language. LLMs are the engines behind most generative AI text applications and behind agentic systems. For Task 1.1 you only need to recognize what each term means and which problem it addresses; the deeper mechanics of foundation models and tokens belong to the generative AI domain, not here. Being able to say "computer vision handles images, NLP handles language, an LLM generates text" is enough to answer definition questions correctly.

This cluster of terms describes how well a model behaves, and it reappears in the responsible-AI domain, so learning it now pays off twice. Bias, in the fairness sense, is a systematic skew in a model's outputs, often caused by unrepresentative training data, that produces unfair results for some groups of people. Fairness is the goal of treating individuals and groups equitably so the model does not disadvantage anyone based on attributes like age, gender, or ethnicity. Bias is the problem; fairness is the objective you pursue by detecting and reducing that bias.

Fit describes how well a model matches the underlying pattern in data, and the exam tests two failure modes. Overfitting happens when a model learns the training data too closely, including its noise, so it performs well on examples it has seen but poorly on new data; it has essentially memorized instead of generalized. Underfitting is the opposite: the model is too simple to capture the real pattern, so it performs poorly even on the training data. The sweet spot, good fit, generalizes well to unseen data. A quick discriminator: great on training data but bad on new data means overfitting, while bad on both means underfitting.

The AIF-C01 exam expects you to tell three learning types apart by the kind of data they use and the problem they solve. Supervised learning trains on labeled data, meaning each example already carries the correct answer, and the model learns to map inputs to those answers. A spam filter trained on emails each marked spam or not-spam is supervised learning. Unsupervised learning trains on unlabeled data and finds hidden structure on its own, with no correct answers provided. Grouping customers into segments by their purchasing behavior, with no predefined categories, is unsupervised learning. Reinforcement learning has no fixed dataset of answers at all; an agent interacts with an environment and learns by trial and error, receiving rewards for good actions and penalties for bad ones. Training a robot to walk or a program to play a game are classic examples.

The single most useful exam clue is the word "labeled": if a scenario says the data is labeled or has known outcomes, think supervised; if it says items must be grouped with no labels, think unsupervised.

Models are only as good as the data they learn from, and the exam asks you to recognize common data categories. The broadest split is structured versus unstructured. Structured data is organized into a predictable format, typically rows and columns, so it is easy to store in tables and query. Two important structured kinds are tabular data (spreadsheet-style rows and columns, such as customer records) and time-series data (values recorded in time order, such as daily temperature or hourly sales). Unstructured data has no fixed schema and includes image, text, and audio, which make up the large majority of data organizations hold.

A second, separate distinction is labeled versus unlabeled data, which ties directly to the learning types above. Labeled data comes with the correct answer attached to each example, for instance a photo tagged "cat," and it is what supervised learning requires. Unlabeled data has no such answers and is what unsupervised learning works with. Labeling is often expensive and time-consuming because it usually needs humans, which is a practical reason teams sometimes prefer unsupervised approaches. Keep the two axes distinct on the exam: structured-versus-unstructured is about format, while labeled-versus-unlabeled is about whether answers are provided.

Once a model exists, there are several ways to run inference, and Task 1.1 asks you to match the style to the workload. Batch inferencing processes a large volume of data all at once, offline, when you do not need an immediate answer, for example scoring every customer overnight to update a churn report. Real-time inferencing returns a prediction immediately for a single request and is used when low latency matters, such as approving or declining a card payment at checkout. Choose real-time when a user or system is waiting on the answer right now.

Asynchronous inferencing suits requests with large payloads or long processing times: the request is queued, processed when resources are ready, and the result is delivered when it is done, so the caller is not blocked while waiting. It is a middle ground between batch and real-time, useful for tasks like processing a large document or video. Serverless inferencing removes the need to manage or pre-provision servers; capacity scales automatically with demand, including scaling down when idle, which fits intermittent or unpredictable traffic where you would rather not pay for idle infrastructure. The exam pattern is straightforward: many records at once and no rush means batch, an instant single answer means real-time, large or slow jobs mean asynchronous, and spiky or occasional traffic with no servers to manage means serverless.

A retailer wants to reduce fraud and understand its customers, and its project touches nearly every Task 1.1 term. Its data science team starts with structured, tabular transaction records. Because past transactions are labeled as fraudulent or legitimate, they use supervised learning: an algorithm processes the labeled history during training and produces a model. To catch fraud the instant a purchase is made, the model is deployed for real-time inferencing at checkout, while a nightly job re-scores dormant accounts using batch inferencing.

Separately, the team has millions of unlabeled product-review texts. With no answers attached, they apply unsupervised learning to group reviews into themes, an NLP task. A computer vision model checks product photos for quality, and a generative AI assistant powered by a large language model drafts replies to customers; when that assistant is allowed to look up an order and issue a refund on its own, it becomes agentic AI. Along the way the team watches for overfitting, so the fraud model does not just memorize old cases, and audits for bias to keep decisions fair across customer groups. Every one of these words is a definition the exam can ask you to recognize.