Machine Learning (ML) uses various algorithms to help computers learn from data and make accurate predictions or decisions. Some of the most widely used Machine Learning algorithms include:
1. Linear Regression – Used for predicting continuous values such as prices, sales, and trends.
2. Logistic Regression – A classification algorithm used when the outcome is categorical, like yes/no or spam/not spam.
3. Decision Tree – Splits data into branches based on conditions and works well for both classification and regression tasks.
4. Random Forest – An ensemble technique that combines multiple decision trees for higher accuracy and better performance.
5. Support Vector Machine (SVM) – Finds the best decision boundary between classes, useful in classification problems.
6. K-Nearest Neighbors (KNN) – Classifies data points based on the closest training examples in the feature space.
7. Naive Bayes – A probabilistic algorithm commonly used in text classification and spam detection.
8. K-Means Clustering – An unsupervised algorithm that groups similar data points into clusters.
Understanding these algorithms is essential for building effective ML models and solving real-world problems across industries. If you want structured learning and hands-on practice with these techniques, check out the Machine Learning Course in Mumbai offered by Seven Mentor. It covers key algorithms with practical projects and industry-focused training.
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