Every one of us has come across the terms of Artificial Intelligence, Machine Learning and Deep Learning. But what are these technologies, and what is the difference between them. In this session, we will dive into some of these concepts and understand the essential difference between these using examples.
What is Artificial Intelligence?
Before going forward, let us discuss what Human Intelligence is? Intelligence is the ability to acquire and apply knowledge and skills based on past experiences. We humans act according to our experiences to make efficient decisions and predictions.
Artificial Intelligence refers to the state of machines in which they are programmed to think and mimic like humans. Machines can do all the activities which humans do if they are trained accordingly.
There are many subdomains in AI, namely Machine Learning, Machine Vision, Natural Language Processing, Robotics, Speech to text, etc.
What is Machine Learning?
Machine Learning is the subdomain of Artificial Intelligence-based on making decisions, predicting future trends, and identifying patterns from the data taken as past experiences, with minimal human interventions.
Machine Learning can be categorized into two parts -
1. Predictive analytics
2. Deep Learning
There are some of the downfalls of Machine Learning predictive analysis, which the concept of Deep Learning can overcome.
We will look at Deep Learning in the next section.
What is Deep Learning?
Deep Learning is the subset of Machine Learning based on Artificial Neural Network which tries to mimic the human brain.
Predictive analysis of Machine Learning cannot perform well on a large number of data-set as well as on unstructured data, whereas deep learning handles these issues.
Difference between AI, ML and DL
Artificial Intelligence is a broader view where it covers many things in the domain of automation. As the name suggests, it can do all the possible things which human do for better optimization. One of the fundamental difference between Machine Learning is that in ML, we need to manually select the features to train the model. Simultaneously, in the case of Deep Learning, it learns the features itself using a neural network. Check out the detailed difference steps here.
Getting started in the AI domain
One can start in the Artificial Intelligence domain from any of its subset mentioned above, but usually, beginning from Machine Learning, predictive analysis is considered adequate.
Below is the timeline which we can consider to dive into the AI domain.
This was all about the basic understanding of AI, ML and DL. It is highly recommended to refer to Google if some or many terms you don't understand.
In the next session we will discuss the ideal timeline and order to learn Data Science effectively and with more productivity.
Please provide your critical feedback through CONTACT US section so as to improve our services.