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Artificial intelligence is an umbrella term that includes machine learning and deep learning. Deep learning follows a
specific learning approach, which is a subset of machine learning comprising multiple machine learning algorithms.
Let us understand the difference between artificial intelligence, machine learning and deep learning.
Artificial Intelligence Machine Learning Deep Learning
It aims at making a machine It aims at making a machine that It aims at building neural network that can
that mimics human can learn through data and solve help in discovering patterns or trends.
intelligence. complex problems.
It is a subset of data science. It is a subset of AI. It is a subset of machine learning.
It is the simulation of It is the training of machines to It is the process of using artificial neural
intelligence in machines. take decisions with experience. networks for solving complex problems.
Examples: Robotics, natural Examples: Decision trees, random Examples: Convolutional neural networks
language processing, forests, support vector machines, (CNNs), recurrent neural networks (RNNs),
computer vision, expert neural networks, etc. generative adversarial networks (GANs),
systems, etc. etc.
Data Modelling Techniques
In AI modelling, we develop different algorithms called models, which can be trained to produce intelligent output.
In other words, we write code to make a machine artificially intelligent.
AI modelling techniques can be broadly classified into two approaches, namely, rule-based and learning-based.
Let us learn about them in detail.
Rule-Based Approach
Rule-based approach is based on a set of rules and set of facts already fed to the machine to generate the desired
output. These models can operate with simple basic information and data. The relationships or patterns in the data
is defined by the developer. To explain it further, let's take an example. You have a dataset comprising 100 images
of cars and 100 images of cycles. To train your machine, you feed this data and label each image as either a car or
a cycle. Now if you test the machine with an image of a car, it will compare with the trained data and according to
the labels of the trained data it will identify it as a car. This is called a rule-based approach. The rules given to the
machine in this example are the labels assigned to the training data.
Labelled Rule-based Training
Datasets Approach Data Output
Used to Train
Machine Used for Testing Machine Identifies the
Model Testing Data Image as Car
Learning-Based Approach
Learning-based approach refers to a model where the relationships or patterns in the data are not explicitly defined
by the developer. In this approach, random data is fed into the machine and the machine develops its pattern or
trends based on the data outputs. It is an alternative method to address some of the challenges of rule-based
systems. This approach is typically followed when the dataset fed to the machine is unlabelled and too random.
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