🚀 Machine Learning Algorithms Every Data Scientist Should Know
Machine Learning isn’t about memorizing algorithms—it’s about knowing when and why to use the right one.
🔹 Supervised Learning
• Classification: Logistic Regression, KNN, Decision Tree, Random Forest, SVM, Naive Bayes
• Regression: Linear, Lasso, Multivariate Regression
🔹 Unsupervised Learning
• Clustering: K-Means, DBSCAN
• Dimensionality Reduction: PCA, ICA
• Association: Apriori, FP-Growth
• Anomaly Detection: Isolation Forest, Z-Score
🔹 Semi-Supervised Learning
• Self-Training • Co-Training
🔹 Reinforcement Learning
• Q-Learning • Policy Optimization • Model-Free & Model-Based Learning
💡 The real skill:
Data → Problem → Algorithm → Evaluation → Optimization
📌 Save this as a quick ML reference and keep learning!
