Common Machine Learning Algorithms

There are numerous machine learning algorithms, each suited for different types of tasks and data. Here are a few common ones:

• Linear Regression:

  A supervised learning algorithm used for predicting a continuous value (regression). It assumes a linear relationship between input features and the output variable.

• Logistic Regression:

  Despite its name, it's a supervised learning algorithm used for classification tasks (predicting a category). It predicts the probability of a binary outcome.

• Decision Trees:

  A supervised learning algorithm used for both classification and regression. It creates a tree-like model of decisions and their possible consequences.

• Random Forest:

  An ensemble learning method that operates by constructing multiple decision trees during training. It's used for both classification and regression and often provides higher accuracy than a single decision tree.

• Support Vector Machines (SVM):

  A supervised learning algorithm used primarily for classification tasks. It finds a hyperplane that best separates data points into different classes in a high-dimensional space.

• K-Means Clustering:

  An unsupervised learning algorithm used for clustering. It aims to partition 'n' observations into 'k' clusters in which each observation belongs to the cluster with the nearest mean.

• Neural Networks (Deep Learning):

  Inspired by the human brain, neural networks consist of interconnected layers of nodes (neurons). Deep Learning refers to neural networks with many layers. They are powerful for complex tasks like image recognition, natural language processing, and more. They can be used for supervised, unsupervised, and reinforcement learning.

This is just a small subset, and the field is constantly evolving with new algorithms and variations.