AI vs ML vs DL: Understanding the Differences in Simple Terms

July 8, 2023 | by


You must have heard these terms, Artificial Intelligence (AI) and Machine Learning (ML), but many people are unaware of the difference between them. In this blog post, Let’s find out what is AI/ML, and then jump into how they work

AI vs ML vs DL

  • AI (Artificial Intelligence): AI is about creating smart machines that can think and make decisions like humans. It helps computers do tasks that need intelligence, like recognizing speech or playing games.
  • ML(Machine Learning): ML is a part of AI where computers can learn and improve without being explicitly programmed. It learns from data and makes decisions based on what it learns.
  • DL(Deep Learning): DL is a type of ML that uses special networks, like the human brain, to learn and understand complex data. It’s great for tasks like recognizing objects in images or understanding human language.

In a nutshell, AI is about smart machines, ML is about learning from data, and DL is a special kind of learning using neural networks. All of them make technology more capable and help us in various aspects of our lives, like voice assistants, personalized recommendations, and even self-driving cars

 Read this comprehensive guide on AI vs ML


AI vs ML vs DL


How AI Machines Learn:

To understand how AI machines learn, let’s see the parallel with how humans learn. Humans learn from their surroundings through sensory inputs like vision, hearing, and touch. Similarly, AI machines rely on sensors like cameras, microphones, accelerometers, and more to gather information for learning. The learning process for machines typically involves three major steps:

  • Feature Extraction: In the context of classifying fruits, for example, we humans use visual features like color and shape. Similarly, machines extract features from images captured by cameras. This initial step involves identifying and isolating relevant attributes from the data.
  • Training the Neural Network: Similar to teaching a child, we train machines by providing labeled data. For fruits, we show the machine multiple pictures of apples and associate them with the label “apple.” The machine’s neural network, a complex mathematical model, processes this training data to learn and recognize patterns.
  • Classification: Once the neural network completes training, it becomes capable of classification. If we present a picture of an apple to the machine, it will identify and classify it accordingly. This step demonstrates how the machine has learned to recognize specific objects or concepts.

Neural Network

Think of a neural network as a team of intelligent individuals, each with a unique role and the ability to communicate with each other. Just like a group of colleagues in an organization, they work together to process information and make decisions. Imagine these individuals as tiny brain cells, or neurons, which receive inputs from their environment. They analyze and transform this information before passing it on to their colleagues through connections called synapses. Each neuron has a specific job. They take the input they receive, perform computations or transformations on it, and then share the results with the next set of neurons. It’s like a relay race, with each neuron passing the baton to the next one in line. Now, let’s talk about how they learn. These intelligent individuals are capable of learning from their experiences. When they make mistakes, they can adjust their approach to improve their performance. It’s similar to how we, as humans, learn from our errors and adapt our behavior accordingly. Just as different parts of our brain specialize in different functions, these neurons in the neural network have specific tasks. They collaborate, each contributing their expertise, as the information flows through the network. This collaboration helps refine and process the data, similar to how our brain processes information at various levels of complexity. Remember, this analogy is an attempt to make the concept of neural networks more relatable, but it’s important to recognize that neural networks are much more complex in reality. They involve intricate calculations and algorithms. Nonetheless, thinking of them as a team of intelligent individuals can give us a human-like perspective on how they function.


Understanding the fundamentals of AI and ML is essential as they shape the future. By comprehending the distinction between these terms and the learning process of AI machines, we gain valuable insights into their potential applications and capabilities. After Reading this post I hope everyone can answer the question, What is AI/ML?

If you have any questions or need further assistance, feel free to get in touch






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