Algorithms or Models in Machine learning

Supervised Learning Algorithms

1. K nearest neighbor classification
2. Logistic Regression
3. Linear Regression
4. Naive bayes
5. Support Vector Machine(SVM)
6. Decision tree
7. Random Forest
8. Xgboost

Here K-nn and Logistic regression is classification models. Naive bayes is probability based model. Linear regression is regression model. Remaining all models are useful in classification and regression problems.

Random forest and Xgboost are ensemble models. They uses decision tree as base learner.

Unsupervised Learning Algorithms

1. K-means Clustering
2. Density-Based Spatial Clustering(DB-Scan)
3. Hierarchical clustering

There are lot of other algorithms also present but these models most widely used algorithms in machine learning.

Thanks for reading.