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The working of machine learning model can be illustrated in the following diagram:
Input ML Model Output
Here, the input refers to the historical data provided to the AI model for training. The AI model analyses this data
to identify patterns and relationships, enabling it to predict the output.
Let’s consider an example where all the data is labelled. This labelled data is provided as input to the ML model.
The model learns from this data and then predicts the output.
Labelled Data ML Model Predictions
Triangle
Rectangle Circle
Circle
Triangle Hexagon
Input Test New Input Output
Real-world Examples of Machine Learning
Let us learn about some real-world examples of ML:
• Recommendation Systems: These are a classic example of Machine Learning in real world. These systems
analyse user's data, such as preferences, behaviour, or past interactions, to suggest personalised options. The
platforms, like Netflix, Flipkart, Spotify, etc. use such kind of recommendation systems to help their customers
to find the related products.
• Spam Email Filtering: Machine learning algorithms learn to identify and filter out spam emails by analysing the
patterns in sender's email address and content.
• Image Recognition: When you upload a picture, an automatic tag recognition system used by applications like
Facebook, suggests people to tag. It uses a face recognition algorithm for the same.
• Speech Recognition: We all love to speak out our messages to Siri, Google assistant, Amazon Alexa etc. These
speech recognition devices use machine learning to understand spoken language and convert speech to text
and respond accordingly.
• Anomaly Detection: In medical diagnostics, anomaly detection helps in identifying unusual patient readings,
such as abnormal heart rates or spikes in blood pressure. For instance, by analysing patient data like age,
blood pressure, and heart rate, algorithms such as Isolation Forest can detect irregular patterns. These flagged
anomalies may indicate conditions like arrhythmia or hypertension, enabling early intervention and start timely
treatment.
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