The AI Revolution: Understanding the Difference Between AI, ML, and Deep Learning

• By Kailash Baria
• 15-09-2025
• Artificial Intelligence

Everyone is talking about artificial intelligence, machine learning and deep learning. These terms are saturated in our news feeds, your latest movie, and the smart devices we tap on daily. For many though, these terms remain an indistinguishable mess of high-tech jargon, often confusedly used interchangeably too. Are they the same? If so, how do they relate?

If you are interested in a career in technology, for instance, by completing an Artificial Intelligence Course or taking a Machine Learning Course, it is essential to understand the key differences. In this article we will clarify these issues, to make learning not only clearer, but also to be able to recognize how these three prolific technologies work together.

The Big Picture: What is Artificial Intelligence (AI)?

Think of artificial intelligence as one very large field. AI is the grand goal of creating machines that do the same things that a human being can do with thought: think, reason, learn, perceive, and even act in the world. AI is not new though it feels like it. The desire to create systems that think and mimic humans has been around since the mid-20th century.

AI can be broken down into two main categories:

• Narrow AI (or Weak AI): This represents the AI available today. It is designed and trained to perform one narrow and specific task. Narrow AI could include a chess-playing computer, a virtual assistant such as Siri, or a recommendation on Netflix or YouTube. They do these tasks extremely well, but can't do anything else.
• General AI (or Strong AI): This represents the theoretical AI that could perform any intellectual task that a human could. It could learn, reason, and solve problems in a variety of domains, similarly to how a human can. This level of AI is still in a research state and is a long way to reality.

At its core, AI is about making machines "smart." There are many ways to be "smart," and this can be accomplished through rules, human learning, or even advanced self-learning algorithms. An Artificial Intelligence Course is generally going to work through all of these areas, the philosophical foundations of AI, the various methods, and the ways AI can be successful and useful.

The Subset: What is Machine Learning (ML)?

Now, let's get a little more into the details. Machine learning is a form of artificial intelligence. It is a way of achieving AI. Instead of programming a machine with a layer of rules for every possible occurrence, we educate it to learn by making use of data.

Here's a simple analogy: imagine you want to teach a computer to identify a cat in a picture.

• A classic, non-ML method would be to write a million lines of code, telling the computer about every possible aspect of a cat: "If it has pointed ears and a tail and whiskers, it is a cat." Of course, this approach is very rigid and would break the moment we couldn’t see the entire cat or had a slightly different cat.
• A machine learning approach, on the other hand, would take a thousand labeled images of cats and non-cats and tell the algorithm to use statistical models to find the patterns and correlations by itself. In this way, the algorithm could find a cat in new, unlabelled images with overwhelming accuracy.

This is the fundamental shift of ML: the machine learns the rules from the data itself.

ML is a vast field with different learning paradigms, including:

Supervised Learning: This is the most frequent kind. You give the algorithm a labelled dataset (for example the cat images). The model learns to see the relationship between the input data, and the appropriate output.

Unsupervised Learning: In this case, the data is unlabelled. The algorithm must find patterns or hidden groupings in the data. A classic case is customer segmentation, where the model groups customers based on similarity to their buying behaviours.

Reinforcement Learning: This is a reward-based system and is where the agent learns to complete tasks through small actions in an environment and receives feedback on every action through rewards/penalties. This is also the technique used to train game-playing AI, for example AlphaGo.

A keen Machine Learning Course would delve into these diverse learning types, teaching you how to build and train models for tasks like cataloguing, regression, and clustering. It's a highly hands-on and in-demand skill set.

The Specialized Subset: What is Deep Learning (DL)?

Let's go even further down the rabbit hole. Deep learning is a specific category of machine learning. It is a nuanced and powerful technique that is a step above traditional machine learning. The meaning of "deep" learning comes from the use of multi-layered neural networks.

Neural networks are computer systems based on the human brain (and, more specifically, its structure and function). Neural networks have layers of inter-linked "nodes", or "neurons". The network learns when it adjusts the connections (the weight) between the various layers.

Deep learning machine learning systems are fundamentally different than other ML systems because they can automatically "extract" features. In traditional machine learning, a data scientist would select, or "engineer" the necessary features from the raw data. For example, to identify a car, the data scientist may tell the algorithm to look for tires, windows, and doors.

Deep learning, on the other hand, has the ability to learn these features itself. In a deep neural network for image recognition the first layer may have a task of learning to recognize simple features such as edges and curves. In the second layer, these edges combine to create shapes and corners.

In the next layers, these shapes would form objects such as eyes, noses, or wheels. The ability of deep learning to layer features results in the handling of extremely complex, unstructured data such as images, audio, and text, which it does at an unmatched level of accuracy.

Deep learning has been the driving force behind many of the recent breakthroughs in AI, including:

• Computer Vision: From facial recognition on your phone to the object detection systems in self-driving cars.
• Natural Language Processing (NLP): Powering large language models (LLMs) like ChatGPT, which can understand and generate human-like text.
• Speech Recognition: The technology behind voice assistants like Alexa and Google Assistant.

The Relationship: A Simple Hierarchy

To summarize the relationship between these three concepts, you can think of them as a set of concentric circles.

• Artificial Intelligence (AI) is the outer, largest circle. It's the broad field of creating intelligent machines.
• Machine Learning (ML) is the circle within AI. It's one of the key methods used to achieve AI by teaching machines to learn from data.
• Deep Learning (DL) is the innermost circle. It's a specific, advanced technique within ML that uses multi-layered neural networks to learn from complex data.

This means all deep learning is machine learning, and all machine learning is artificial intelligence. But not all AI is machine learning (i.e. it may be a simple rules-based system) and not all machine learning is deep learning (it may be a simpler algorithm such as a decision tree).

Final Thoughts and Your Next Step

The world of AI is ever-changing and therefore, full of opportunity. Although the lines between these technologies can become blurred, understanding this core framework is the first step to a better understanding of this area of study.

Regardless of whether your interest is more philosophical/theoretical or practical/data-science based, there is a learning path within AI for you. An Artificial Intelligence Course will provide you with a general overview of this whole field. So there is an exact learning path with a Machine Learning Course which provides you with very specific, job-ready skills to be able to build and deploy intelligent models.

Deep learning has provided a big boost to the rapid advancements we are currently experiencing with AI and, therefore, this is an amazing time to get involved in this area of study. By knowing the fundamental differences and links between AI, ML, and DL, you are in a better place to navigate this dynamic environment and be a part of the next wave of disruption.