Machine learning categories

Hey lovelies 🌸

Machine Learning is broadly divided into four categories: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning. Let’s break them down in simple terms:

1. Supervised Learning:

In supervised learning, the model is trained on labeled data — meaning the input data comes with the correct answers (labels). For example, imagine we want a model to predict the likelihood of someone developing lung cancer based on factors like smoking history, gender, etc. These factors are known as independent variables, and the outcome (whether or not the person has cancer) is the dependent variable, which serves as the label the model learns from.

2. Unsupervised Learning:

Here, the model is trained on unlabeled data — there are no predefined outcomes. Using our previous example, this would mean the dataset has only the factors (like smoking history, gender, etc.), but no column stating whether the person has cancer. The model tries to find hidden patterns or groupings within the data on its own.

3. Semi-Supervised Learning:

This approach uses a mix of labeled and unlabeled data. A small portion of the data is labeled, and the rest is not. This helps the model learn from the labeled data and generalize its understanding to the larger set of unlabeled data.

4. Reinforcement Learning:

This type of learning is based on feedback from the environment. The model (often called an agent) learns by taking actions and receiving rewards or penalties based on those actions. Over time, it learns to make better decisions to maximize rewards. Unlike supervised learning, reinforcement learning does not rely on labeled input/output pairs, but rather on experience and feedback.

In conclusion, all machine learning methods depend on data — whether labeled or unlabeled — and the type of data determines which learning approach is most suitable.

Thanks for reading, and see you in the next one! 🌸