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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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