Machine learning algorithms are the core of the AI field, providing the intelligence for computers to learn from data and make decisions. Here are the top 10 machine learning algorithms that you should know:
Linear Regression - Simple yet powerful, it helps in understanding the linear relationship between the variables.
Logistic Regression - An extension of linear regression for binary classification problems.
Support Vector Machines (SVM) - Effective in high-dimensional spaces, it works well with small datasets.
Decision Trees - Simple to understand, yet powerful in predictions.
Random Forest - An ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
k-Nearest Neighbors (k-NN) - A simple, non-parametric algorithm that classifies new cases based on a similarity measure.
k-Means Clustering - A clustering algorithm that divides the dataset into k clusters.
Principal Component Analysis (PCA) - A dimensionality reduction technique that is often used to reduce the dimensionality of large datasets.
Neural Networks - Inspired by the human brain, these algorithms are capable of learning complex patterns from data.
Boosting Algorithms - Such as AdaBoost, Gradient Boosting, and XGBoost, which build a strong classifier from weak ones.
For more information on machine learning algorithms, you can read our detailed guide on Machine Learning Basics.