Artificial intelligence and machine learning are related parts of computer science. These two technologies are the most advanced technologies that are used to create intelligent systems. Although these two technologies are related and sometimes even many of them are used as synonyms for each other and are usually developed using the Python programming language. They find, but still, in various cases, these two terms are completely different. In this article, we will study some main differences between artificial intelligence and machine learning, as well as review information about artificial intelligence and machine learning together.

In general, we can separate artificial intelligence and machine learning as follows:

Artificial intelligence is a larger concept than machine learning and aims to create intelligent machines that can simulate human behavior and thinking ability, while machine learning is a program or subset of artificial intelligence that allows machines to learn data and information without being pre-programmed.

What is artificial intelligence?

Artificial intelligence is a field of computer science that creates a computer system that can imitate human intelligence. Artificial intelligence consists of two words “artificial” and “intelligence”, which means “power of human thinking”. Hence, we can define it as follows:

Artificial intelligence is an advanced technology that can be used to create intelligent systems that can simulate human intelligence.

Artificial intelligence system does not require pre-programming, instead, they use algorithms that can work with their intelligence. Including machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks. Artificial intelligence applications include Siri, Google’s Alpha, Go, games like chess, etc.

In general, based on the ability of artificial intelligence, it can be classified into three types:

  • Weak artificial intelligence
  • General artificial intelligence
  • Strong artificial intelligence

Currently, we work with weak AI and general AI. The future of AI is strong artificial intelligence and is said to be smarter than humans!

What is machine learning?

Machine learning is extracting knowledge from data and information. It can be defined as:

Machine learning is a subset of artificial intelligence that enables machines to learn without prior programming and using previous data and experiences.

Machine learning enables a computer system to make predictions or make decisions without using its past data. In fact, without explicit planning. Machine learning uses large amounts of structured and semi-structured data to generate accurate results or even make predictions based on that data.

Machine learning works on an algorithm that can learn using previous data. But this algorithm works only for specific and limited domains, for example, if we are creating a machine learning model to recognize images of dogs, it will only work for dog images and if we provide new data like cat images, Machine learning will not be the answer.

Machine learning is used in various cases such as online recommender systems, Google search algorithms, email spam filters, chatbots, Facebook dating, etc.

Machine learning is still in its early stages around the world and there are many projects to be done and many things to improve. When you start working on your ideas in machine learning projects, you can not only Test your strengths and weaknesses but you will be exposed and it can be a good opportunity to strengthen your career.

Key Differences between Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence

  1. The goal of artificial intelligence is to create a human-like intelligent computer system to solve complex problems.
  2. Artificial intelligence is a technology that enables a machine to simulate human behavior.
  3. In artificial intelligence, we design intelligent systems to do everything like humans.
  4. Machine learning and deep learning are two main subsets of artificial intelligence.
  5. Artificial intelligence has a very wide scope.
  6. Artificial intelligence is trying to create an intelligent system that can perform various complex tasks.
  7. The concern of the artificial intelligence system is to increase and maximize the chance of success.
  8. Examples of artificial intelligence applications: are Siri, customer support, Expert systems, online games such as chess, intelligent humanoid robots, etc.
  9. Based on capabilities, artificial intelligence can be divided into three types: Weak AI, General AI, and Strong AI.
  10. Artificial intelligence includes learning, reasoning, and self-correction.
  11. Artificial intelligence deals with structured, semi-structured, and unstructured data.

Machine learning

  1. The goal of ML is to enable machines to learn from data so that they can produce accurate results.
  2. Machine learning is a subset of artificial intelligence that allows a machine to automatically learn from past data without explicit programming.
  3. In ML, we train machines with the help of data to do a specific task and get a precise result.
  4. Deep learning is a major subset of machine learning.
  5. Machine learning has a limited scope.
  6. Machine learning is trying to build machines that can only perform specific tasks for which they have been trained.
  7. The concern of machine learning is mainly the learning patterns and the accuracy of their actions.
  8. Examples of machine learning applications include online recommender systems, Google search algorithms, chatbots, making friends in social networks, etc.
  9. Machine learning is divided into three types: supervised learning, unsupervised learning, and reinforcement learning.
  10. Machine learning involves learning and correcting itself with new and old data.
  11. Machine learning only deals with structured and semi-structured data.


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