Unsupervised ML Methods

- Overview

Unsupervised machine learning (ML) is a technique that uses artificial intelligence (AI) algorithms to train models on unlabeled data. The model is given unlabeled data and allowed to discover patterns and insights without human supervision.

Here are some uses for unsupervised ML:

• Identifying patterns and trends in raw datasets.
• Clustering similar data into a specific number of groups.
• Understanding datasets in the early exploratory phase.
• Monitoring data quality.
• Identifying issues before a problem
• Preprocessing data for other machine learning functions

During the learning phase, an unsupervised network tries to mimic the data it's given and uses the error in its mimicked output to correct itself. 

Some disadvantages of unsupervised ML include:

• Sorting and output are not easy to accurately define.
• Results are not always useful because there isn't an output measure or label.
• It can also cost more since human intervention is often necessary to make sense of patterns

Unsupervised ML models work in three stages: 

• Collect the data that's needed.
• Train the model to make sense of the unlabeled data.
• Evaluate the model to see how it performs for a given set of inputs.

Please refer to Wikipedia: Unsupervised Learning for more details.

- Unsupervised ML Algorithms

Unsupervised learning is a machine learning (ML) technique where the user does not need to supervise the model. Instead, it allows the model to work on its own to discover previously undetected patterns and information. It mainly deals with unlabeled data.  

Unsupervised ML algorithms allow users to perform more complex processing tasks than supervised learning. Although, unsupervised learning can be more unpredictable than other natural learning methods. Unsupervised ML algorithms include clustering, anomaly detection, neural networks, and more.. 

The three types of unsupervised ML tasks are: Clustering, Association rules, Dimensionality reduction. 

Here are some examples of unsupervised ML methods: 

• Dimensionality reduction: Condenses data into fewer features while retaining as much information as possible. An auto-encoder uses a neural network for dimensionality reduction.
• Association rules: A rule-based method that discovers relationships and associations between different variables in large-scale datasets. Association rule learning can analyze shopping carts, marketing strategy, and other event-related tasks.
• Principal Component Analysis (PCA): Uses patterns present in high-dimensional data to reduce the complexity of the data while retaining most of the information.

Other unsupervised ML methods include: 

• K-means clustering
• KNN (k-nearest neighbors)
• Hierarchal clustering
• Anomaly detection
• Neural Networks
• Independent Component Analysis
• Apriori algorithm

- Why Unsupervised Learning?

Unsupervised machine learning (ML) is a technique that uses AI algorithms to train models on unlabeled data. The model is given unlabeled data and allowed to discover patterns and insights without human supervision.

Following are the main reasons for using unsupervised learning in ML:

• Unsupervised ML discovers various unknown patterns in data.
• Unsupervised methods help you find features that can be used for classification.
• It happens in real-time, so all input data is analyzed and labeled in the presence of the learner.
• It is easier to get unlabeled data from a computer than labeled data that requires human intervention.

- Benefits of Unsupervised Learning

There are many benefits to using unsupervised ML algorithms on your data. Here are some of the most common reasons people turn to unsupervised ML:

• It can handle large amounts of unlabeled and unstructured data.
• It makes analyzing complex data easier and faster.
• It is able to identify previously undetected patterns.
• It knows your data, so it can teach you things it doesn't know.

Unsupervised learning can help humans make better decisions, which is why businesses turn to Unsupervised, the largest supplier of this type of AI, to gain ultra-specific insights on their most complex data sets. 

The Unsupervised platform acts as a teacher, measuring how people learn and understand data to provide better insights.

- Disadvantages of Unsupervised Learning

[More to come ...]