Machine Learning Techniques

Machine learning techniques refer to the different approaches and methods by which machines learn from data and make decisions or predictions. ML techniques are broadly classified based on the type of learning they involve and the nature of the data they work with. We can divide the ML techniques into supervised, unsupervised, and reinforcement learning.

In this lesson, we’ll understand the unique applications, methodologies, and strengths of the ML techniques that make them suitable for different tasks in real-world scenarios.

Supervised learning

Supervised learning is a technique where an algorithm is trained on labeled data, meaning each input comes with a corresponding output. The model learns by example, using the labeled data to make predictions or classify new, unseen data. Supervised learning requires a “supervisor” or guide (in the form of labeled data) to help the algorithm learn associations between inputs and outputs.

The algorithm analyzes the labeled dataset during training, learning patterns, or relationships between inputs and their respective labels. Once trained, it can predict labels for new inputs based on what it learned. The key here is that the model learns a mapping from inputs (features) to outputs (labels).

Example: Email spam detection

A common example of supervised learning is email spam detection. In this scenario, we train our ML on a labeled dataset of emails where each email is categorized as “spam” or “not spam.” The ...