Demystifying Unsupervised Machine Learning

Demystifying Unsupervised Machine Learning


Unsupervised machine learning is a branch of machine learning where the model learns patterns from unlabelled data without any explicit supervision or guidance. This technology has several applications in automating processes and is a discipline covered in any advanced Data Science Course.

Unsupervised Machine Learning

Here is a breakdown that will demystify unsupervised machine learning, its usage, and what challenges its application face.

Characteristics and Applications

  • No Labels Required: In unsupervised learning, the dataset consists of input data without corresponding output labels. The model’s task is to find hidden structures or patterns within the data without being explicitly told what to look for.
  • Clustering: One common task in unsupervised learning is clustering, where the goal is to group similar data points together. Data scientists commonly use algorithms like k-means, hierarchical clustering, and DBSCAN for clustering tasks. These algorithms are covered in any advanced course in data science such as a Data Science Course in Pune.
  • Dimensionality Reduction: Another common task is dimensionality reduction, where the goal is to reduce the number of features while preserving the most important information. Techniques like principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and autoencoders are usually for dimensionality reduction.
  • Anomaly Detection: Unsupervised learning can also be used for anomaly detection, where the model identifies data points that deviate significantly from the norm. Techniques such as isolation forests, one-class SVM, and Gaussian mixture models are employed for anomaly detection. Anomaly detection is of specific significance for research students and cyber security professionals and is taught in detail in any research-oriented Data Science Course.
  • Generative Modelling: Unsupervised learning includes generative modelling, where the model learns to generate new data samples similar to those in the training set. Generative adversarial networks (GANs), variational autoencoders (VAEs), and restricted Boltzmann machines (RBMs) are examples of generative models.
  • Evaluation: Evaluating unsupervised learning models can be challenging since there are no explicit labels to compare predictions against. Evaluation metrics depend on the specific task but may include measures of cluster cohesion, silhouette score, reconstruction error, or novelty detection performance.
  • Applications: Unsupervised learning has numerous applications across various domains. A domain-specific Data Science Course will cover unsupervised learning as applied in a particular business or industry domain:
  • Market Segmentation: Clustering customers based on their purchasing behaviour.
  • Anomaly Detection: Detecting fraudulent transactions in financial data.
  • Recommendation Systems: Grouping similar items or users based on their preferences.
  • Image and Text Analysis: Extracting meaningful representations from raw data without labels.


Unsupervised learning can be challenging due to the lack of labelled data for training and evaluation.

It requires careful preprocessing, feature engineering, and model selection to extract meaningful insights from the data.


Unsupervised machine learning plays a crucial role in discovering hidden patterns, extracting useful representations, and exploring the structure of complex data without explicit supervision. By leveraging clustering, dimensionality reduction, anomaly detection, and generative modelling techniques, unsupervised learning enables data-driven insights and discoveries across a wide range of applications. With the demand for automation increasing in every business process, the demand for professionals who have expertise in unsupervised learning is also increasing and more and more learning centres are offering a Data Science Course that has adequate coverage on unsupervised machine learning.

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