Beginners Guide: Difference Between Machine Learning And Artificial Intelligence


Artificial Intelligence (AI) and Machine Learning (ML) are two of the most well-known buzzwords in emerging technologies and are often used interchangeably. They have become a part of our regular day to day life, however that doesn’t mean that we understand them well. A lot of confusion exists between AI ML, including the difference between Machine Learning and Artificial Intelligence (AI and ML).

Let us explore some of the main differences between AI and Machine Learning in this article.

Artificial Intelligence Vs Machine Learning

  1. What is Artificial Intelligence?
  2. What is Machine Learning?
  3. What Is The Difference Between Machine Learning And Artificial Intelligence?

1. What is Artificial Intelligence?

Artificial intelligence is the study to train computers to do things that humans do better or to imitate human intelligence. Artificial intelligence is, in general, utilized in circumstances where adjusting to new situations is essential. Artificial Intelligence learns by gaining information and figuring out the application of that information. The point of AI consciousness is to expand the odds of success and to locate an ideal solution for humankind.

2. What is Machine Learning?

In Machine learning, algorithms gain information or expertise through experience. ML depends on big data collections to discover common patterns. For instance, say you develop a program that reads several data inputs (images) regarding various types of skin conditions. An ML algorithm can help a machine learn about the various skin conditions and identify it. After learning from the images, a machine can now decide on its own how a particular skin infection looks like. The algorithms inspect pictures and distinctive designs that exist between these pictures that have comparable conditions.

There are three approaches to Machine Learning. 

  1. Supervised learning: Just like how it sounds, supervised learning includes training a machine by presenting several example inputs and their desired outputs. In this process, this data set is presented by a ‘supervisor’. 
  2. Unsupervised learning: Here, there is no involvement of any supervisor. The algorithm is allowed to learn on its own and find any structure in the inputs. 
  3. Reinforcement learning: This includes the involvement of a dynamic environment. Here, a computer system is expected to learn from its environment and perform a certain task. There are constantly learning and feedback involved in reinforcement learning.

3. What Is The Difference Between Machine Learning And Artificial Intelligence?

Let’s understand the difference between AI ML through the below-mentioned points.

A) Definition 

AI is defined as the ability to gain and implement knowledge, while ML is defined as the addition of knowledge or skill. AI ML are not very different. In fact, Machine Learning is a subset of AI. Machine Learning explores how a computer system can perform a certain task accurately by learning from the inputs or the environment. It is focused on developing smart computer programs. Artificial Intelligence focuses on the application of such smart programs to develop intelligence in machines.

B) Aim

AI aims to increase the chance of success and not accuracy; on the other hand, machine learning tends to increase accuracy and doesn’t concern success.

C) Functioning

AI acts like a computer program that accomplishes intelligent work. The objective is to mimic natural knowledge to find solutions for complex problems. AI is dynamic and leads to developing a framework to mimic human intelligence. While ML uses the simple concept of collecting data and learning from it. The objective is to gain data on a specific task to maximize the machine’s performance. ML enables systems to take in new things from the data. It includes making self-learning algorithms.


Though Machine Learning and Artificial Intelligence (AI and ML) sound different, they are closely interconnected. AI is the concept of computers being able to perform tasks in a way that we would consider ‘smart.’ While ML is the current application of AI-based machines, learning and improving through collecting Big Data. Voice assistants like Alexa, Siri, and Google Assistant are a few examples of AI-based applications. On the other hand, Netflix recommendations, Amazon Prime shopping recommendations are examples of applications of Machine Learning. 

Machine Learning & AI holds a promising future for anyone who is interested in playing with data and technology. If you wish to build a future in AI and ML, check our online self-paced 3-month-long Full Stack Machine Learning and AI Program

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