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What is Modelling?
Modelling or data modelling is defined as the process of designing decision-making algorithms that have to
be trained on a set of data (which was acquired at the data acquisition stage for the problem you scoped in the
problem-scoping stage) and applying that learning to recognise certain types of patterns.
Once the data is visualised and trends are formed, we need to work with algorithms to prepare the AI model. This
can be done by designing our models or using the existing AI models. Before we go into the details of modelling,
let us first understand the following important terms:
• Artificial Intelligence: AI refers to any technique that enables computers to mimic or imitate, develop, and
demonstrate human intelligence. They are machines that can perform tasks that they are programmed for. AI
enables machines to think without any human intervention.
• Machine Learning: Machines need to learn the ways of humans by learning the techniques and processes. So
machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to
improve with experiences. So machines learn from their mistakes and take them into consideration in the next
iteration, this way they keep improving with experience. For example, Snapchat filters and Netflix
recommendations.
Artificial
• Deep Learning: Machines can draw meaningful inferences from large Intelligence
volumes of datasets. In deep learning, the machine is trained with a huge
amount of data, which helps it train itself. Deep learning is a machine Machine
Learning
learning algorithm that is inspired by the functionality of our brain cells
called neurons. For example, Google Translate and image recognition in Deep
social media apps. Learning
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 that It aims at making a machine that It aims at building neural network
mimics human intelligence. can learn through data and solve that can help in discovering patterns
complex problems. or trends.
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 intelligence It is the training of machines to It is the process of using artificial
in machines. take decisions with experience. neural networks for solving complex
problems.
Examples: Robotics, natural Examples: Decision trees, random Examples: Convolutional neural
language processing, computer forests, support vector machines, networks (CNNs), recurrent neural
vision, expert systems, etc. neural networks, etc. networks (RNNs), generative
adversarial networks (GANs), etc.
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