🚀 Machine Learning Algorithms Every Data Scientist Should Know
Machine learning is more than just building models—it's about choosing the right algorithm for the right problem.
Here's a quick overview:
📌 Supervised Learning
• Classification: Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes
• Regression: Linear Regression, Lasso Regression, Multivariate Regression
📌 Unsupervised Learning
• Clustering: K-Means, DBSCAN
• Association: Apriori, Frequent Pattern Growth
• Anomaly Detection: Isolation Forest, Z-Score
• Dimensionality Reduction: PCA, ICA
📌 Semi-Supervised Learning
• Self-Training
• Co-Training
📌 Reinforcement Learning
• Model-Free Learning
• Model-Based Learning
• Q-Learning
• Policy Optimization
Learning when to use each algorithm is just as important as knowing how it works.
Save this roadmap for quick revision and share it with anyone preparing for Data Science or Machine Learning interviews. 📚
July 20, 2026 398 5
