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Supervised Learning
Supervised learning is a type of machine learning where the model learns from labelled data, meaning the input data
is accompanied by the correct output. In supervised learning, the algorithm learns to map input data to output labels
based on sample input-output pairs provided during the training phase. The goal is to learn a mapping function
from input variables to output variables so the model can make predictions on unseen data. Examples of supervised
learning algorithms include linear regression, logistic regression, decision trees, Support Vector Machines (SVM), and
neural networks.
This approach is widely used in various real-world applications. For instance, in email filtering, supervised learning
algorithms can classify emails as spam or non-spam based on labelled data. In finance, supervised learning models
predict stock prices or assess credit risk. In healthcare, these algorithms help to diagnose diseases by analysing
patient data with known outcomes, improving both accuracy and efficiency in medical predictions.
Labelled Data
Prediction Carrot
Bell
Pepper
Model Training Tomato
Labels
Tomato
Bell
Carrot Pepper Test Data
Unsupervised Learning
Unsupervised learning is a type of machine learning where algorithms are used to analyse and cluster unlabelled
datasets. In unsupervised learning, the algorithm tries to find hidden patterns or structures in the input data without
explicit guidance. The goal is to explore and discover inherent structures or relationships within the data, such as
clusters, associations, or anomalies. Examples of unsupervised learning algorithms include k-means clustering,
hierarchical clustering, Principal Component Analysis (PCA), and autoencoders.
This approach is useful in various real-life applications. For example, in market segmentation, unsupervised learning
can identify distinct customer groups based on purchasing behaviour, helping businesses tailor marketing strategies.
In genomics, these algorithms can uncover patterns in DNA sequences, aiding in understanding genetic influences
on diseases. In cybersecurity, anomaly detection algorithms can identify unusual patterns of activity, helping to detect
potential security breaches or fraudulent behaviour.
Input Raw Data Outputs
Interpretation Processing
Algorithms
Unlabelled Data
128 Touchpad Artificial Intelligence (Ver. 3.0)-XI

