Machine learning (ML) techniques

Machine learning (ML) techniques are methods or approaches used to construct models that learn patterns and relationships from data to make predictions or decisions. These techniques can be categorized into major groups based on the type of learning they involve and the problems they address.

Here’s a brief overview of some commonly used machine learning techniques.

Supervised Learning

Supervised learning is a type of machine learning where a model learns to map input data to labeled outputs by finding patterns in the data. It requires a dataset with both inputs (features) and their corresponding outputs (labels) for training. The model uses this labeled data to make predictions about unseen inputs by minimizing the error between predicted and actual outputs. Supervised learning techniques are widely used in tasks such as classification (e.g., determining whether an email is spam or not) and regression (e.g., predicting house prices based on size, location, and other factors). It is one of the most common and well-understood approaches in machine learning due to its practical applications and measurable accuracy.

Credit Wikipedia.org

Unsupervised learning

Unsupervised learning is a branch of machine learning which enables models to analyze and discover patterns in unlabeled data. Unlike supervised learning, where predefined outputs or labels are provided, unsupervised learning aims to uncover hidden structures, relationships, or groupings within the data. Techniques such as clustering (grouping customers based on purchasing behavior) is commonly employed in unsupervised learning. This technique also have applications in anomaly detection, market segmentation, and data exploration, assisting in identifying valuable insights without the requirement of labeled data.