Unsupervised machine learning.

Unsupervised learning is a branch of artificial intelligence and a type of machine learning that learns from unlabeled data without human supervision and allows for insights about the data and patterns without any explicit guidance or instruction.
Main points to note

• Unsupervised learning allows the model to learn by discovering patterns and relationships in unlabeled data.

• Clustering algorithms group similar data points together based on their inherent characteristics.

• Feature extraction captures esential information.

• Label association assigns categories to the clusters based on the extracted patterns and characteristics.

A perfect example of how to illustrate this is to imagine a machine learning model trained on a large dataset of unlabeled patients with different diseases. Your task is to use unsupervised learning to identify different types of diabetes patients, be it type 1, type 2, or gestational diabetes, from the unseen data.Thus, the machine has no idea about the features of the patients. So we can’t categorize a specific type of diabetes among the patients. But it can categorize them according to their similarities, patterns, and differences.

Types of Unsupervised Learning
Unsupervised learning is classified into two categories of algorithms:

Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
Clustering is a type of unsupervised learning that is used to group similar data points together. Clustering algorithms work by iteratively moving data points closer to their cluster centers and further away from data points in other clusters.

• Exclusive (partitioning)

• Agglomerative

• overlapping

• Probabilistic
  Clustering Types:-
  Hierarchical clustering
  K-means clustering
  Principal Component Analysis
  Singular Value Decomposition
  Independent Component Analysis
  Gaussian Mixture Models (GMMs)
  Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
  An association rule based on machine learning is to determine the probability of the co-occurrence of items in a collection.A good example is finding out which products were purchased together.

In conclusion, unsupervised machine learning does not have evaluation metrics; therefore, it does not have a feedback mechanism.