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Learning-based Approach
Model
Model Training
Unlabelled Data Clustering on the Basis of Size
A learning-based approach in AI allows the machine to train on data and adapt its model dynamically. It
modifies itself based on data changes, ensuring adaptability and handling exceptions effectively.
For example, a learning-based product recommendation system is an AI model used by e-commerce platforms
like Amazon or Flipkart to suggest products to users based on their behaviour, preferences, and purchase
history. Unlike rule-based systems that suggest predefined items, this approach learns patterns and adapts to
users' interests over time.
The system is trained using a large dataset of user interactions, that includes browsing history, purchase history,
search queries and reviews/rating. The trained model analyses a user’s current activity in real time and matches it
with patterns learned during training. Using machine learning algorithms, the recommendation system identifies
shifts in user interests or seasonal preferences. This learning-based recommendation system enables e-commerce
platforms to offer a personalised shopping experience, boosting user satisfaction and business revenue.
The Learning-based Approach can further be divided into three sections:
Learning-based Approach
Supervised Learning Unsupervised Learning Reinforcement Learning
Supervised Learning
Supervised Learning is a type of machine learning where a model is trained
on a labelled dataset. A labelled dataset is the information which is tagged
with identifiers of data. Labels are the key component in supervised
learning, as they guide the learning process. A label is an information
that can be used as the tag for data. For example, students in a class are
evaluated based on their performance in exams and assignments. Their
performance is categorised into labels such as "Outstanding," "Very Good,"
"Satisfactory," or "Needs Improvement."
Supervised learning is like, how a teacher helps students learn. The teacher provides clear examples and guidance
(training data) to teach concepts. Later, the teacher tests the students' understanding with new questions (testing
data). Similarly, a supervised learning model uses labelled training data to learn patterns and then applies this
knowledge to make predictions on unseen data, improving its accuracy over time.
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