Updated: May 8


Artificial Intelligence is a vast field of study, and it has lots and lots of different branches. But today, the main focus will be on Machine Learning and Deep Learning. Machine Learning and Deep Learning are widely used branches in Artificial Intelligence lately. Both the fields are so extensive that they are to be studied separately in specialization programs in universities / higher education. Although each of them is a unique and vast subject in itself, they are still the subset of Artificial Intelligence; the superset of both of them.

At present, machine learning tools assist us everywhere on our phones, desktops, or other devices through the internet. Similarly, we are also using Deep Learning powered tools on our devices. It seems that ML and DL are substantially significant fields that we should be aware of before we get into Artificial Intelligence. Let us take a closer look at each of them:


Machine learning is a subset of Artificial Intelligence and focuses on predictive tasks. It's a branch of Artificial Intelligence based on the notion that machines can feed on existing data, figure out patterns and make decisions with minimal human intervention. Machine learning is such a part of computer science where you do not have to hard-code everything to make the computer do various tasks for you. In the past decade, machine learning has given us the power that has dramatically improved the lives of humans. Now, thanks to machine learning, we have self-driving cars and speech recognition systems.


In addition to that, machine learning has helped humans to hugely understand the human genome that, without Machine Learning, could have taken a lot of time. We can effectively search anything on the internet, and magically, we get the content on top that we are most used to, always! Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers believe that it is the best way to develop human-level AI. Our objective for machine learning is that it should learn and generalize from its experience. Generalization is that the machine should make decisions or perform tasks accurately on unseen data or environment. The training data comes from uneven probabilistic distribution, and the computer must generalize over the data and figure out some pattern to predict substantially accurate results on new data/environment. In Machine Learning, the machine learns from finite training data, and the future is uncertain. Therefore, ML algorithms don't guarantee accurate results for future tasks/environments.

Machine Learning various kinds of learning types like:

(i) Supervised learning

(ii) Unsupervised learning


There are much more than these. Check out this post to learn more.


Deep learning is a subset of Machine learning which is considered a subset of AI, and so does Deep learning. Deep learning is a field of science where we build mathematical intuition behind some problem and try to put it in an Artificial Neural Network to mimic the functionality of humans neural network. Human Neural Network has been the inspiration for the notion of Artificial Neural Network, but they are not entirely similar. The ANNs are akin to HNN due to a mere reason, and that is the human neurons are densely connected, which helps humans perform complex tasks.