Artificial Intelligence (AI) and Machine Learning (ML) have become the buzzwords for the 21st century. Everywhere you look, some or the other is "AI-Powered" or "Driven by ML". These words have become so synonymous with each other that they are often used interchangeably. Machine Learning, however, is a subset of what AI is. In this article, we’ll take a look at two of the most prominent and famous branches of AI - Machine Learning and Neural Networks. Let’s take a look at what these are and how they differ from each other.
Machine Learning falls under the general umbrella of Artificial Intelligence. Machine Learning essentially builds intelligent machines or systems that are able to automatically learn and train themselves continuously through experience, without a human having to give the machine explicit instructions.
Machine learning is continuously evolving and aims to understand the data structure of the dataset at hand to accommodate the data into relevant models. When talking about Machine Learning models one is generally referring to more statistical based models, like regression or clustering. Machine learning is classified into two major categories - supervised learning and unsupervised learning.
Classical machine learning requires more ongoing human intervention to get results, and it tends to be less computationally demanding, hence, not requiring any special setup for computation. They are more easily interpretable and explainable as compared to neural networks.
Neural networks, as the name suggests are modeled after the human brain. They bring to the table a more sophisticated approach to Machine Learning. Technically, if you look at it, neural networks form under the umbrella of machine learning. However, they are more sophisticated than the algorithms generally considered under machine learning.
The terms neural networks and deep learning are used interchangeably. Neural networks have multiple layers of neurons in them. Any neural network with multiple layers (generally more than 3 layers) is called a deep neural network. These networks are generally classified based on their architecture. More often than not these architectures have very specific use cases. The most famous categories of architectures are:
Deep learning is a lot more complex than classical machine learning during the set up and building phase but requires minimal intervention thereafter. This also means that neural networks are far more complex and require a lot of data to train on, which in turn requires a higher compute power and a more sophisticated set up.
Although deep learning models take a lot more time to set up when compared to classical machine learning approaches, they can generate results instantaneously. However, it is worth noting that their results improve over time as more and more data becomes available.