10 Algorithms Machine Learning Engineers Need to Know
1. Linear Regression: Basic model for predicting numeric values based on linear relationships.
2. Logistic Regression: Used for binary classification problems, outputs probabilities.
3.
Decision Trees
: Hierarchical tree structures for classification and regression.
4. Random Forest: Ensemble of decision trees, reduces overfitting.
5. Support Vector Machines (SVM): Effective for both linear and non-linear classification.
6. K-Nearest Neighbors (KNN): Non-parametric method for classification and regression.
7. K-Means Clustering: Unsupervised algorithm for clustering data points.