Machine Learning Methods

Machine learning methods can be broadly classified into four types: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning.

1. Supervised Learning: In this method, the model is trained on a labelled dataset. That is, the correct answer (label) is provided for each example in the training dataset. The model learns to predict the output from the input data during the training process. Once the model is trained, it can be used to predict the output for unseen data. Examples of supervised learning algorithms include Linear Regression, Decision Trees, K-Nearest Neighbors, and Support Vector Machines.

2. Unsupervised Learning: Unlike supervised learning, in unsupervised learning, the model is trained on an unlabelled dataset. The model learns to identify patterns and relationships in the input data without any prior knowledge about the output. The main aim of unsupervised learning is to find the underlying structure of the dataset. Examples of unsupervised learning algorithms include K-means Clustering, Hierarchical Clustering, and Principal Component Analysis.

3. Semi-supervised Learning: This method is a combination of supervised and unsupervised learning. The model is trained on a partially labelled dataset. The model learns to predict the output from the labelled data and identifies patterns and relationships from the unlabelled data. This method is useful when labelling data is costly or time-consuming.

4. Reinforcement Learning: In this method, an agent learns to behave in an environment by performing certain actions and observing the results/rewards of those actions. The aim of the agent is to maximize the total reward. The agent learns from its experiences, i.e., it learns to choose the best action based on the current state and the possible reward. Examples of reinforcement learning algorithms include Q-Learning, Deep Q Network, and Policy Gradients.