Page 226 - AI Ver 1.0 Class 9
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Till now, you have learnt about problem scoping, data acquisition and data exploration. After data exploration,
the next step in the AI project cycle is data modelling.
What is Modelling?
Modelling or data modelling is defined as the process of designing decision-making algorithms that has 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 apply that learning to recognise certain types of patterns.
Once the data is visualized and trends are formed, we need to work with algorithms to prepare the AI model.
This can be done by designing our own 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.
• Deep Learning: Machines can draw meaningful inferences from large Artificial
volumes of datasets. In deep learning, the machine is trained with a huge Intelligence
amount of data which helps it train itself. Deep learning is a machine Machine
learning algorithm that is inspired by the functionality of our brain cells Learning
called neurons. For example, Google translate, image recognition in social
media apps. Deep
Artificial intelligence is an umbrella term that holds machine learning and Learning
deep learning. Deep learning follows a specific learning approach which is a
subset of machine learning comprising multiple machine learning algorithms.
Difference between AI, Machine Learning and Deep Learning
Let us understand the difference between artificial intelligence, machine learning and deep learning.
Artificial Intelligence Machine Learning Deep Learning
AI aims at making a machine that Aims at making a machine that Aims at building neural network
mimics human intelligence. can learn through data and solve that can help in discovering
complex problems. patterns 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.
224 Touchpad Artificial Intelligence-IX

