📌 Machine Learning Algorithms You Should Know
Machine Learning isn’t just about models—it’s about choosing the right approach for the problem.
Here’s a quick breakdown 👇
🔹 Classification (Categories)
Logistic Regression, Naive Bayes, KNN, SVM, Decision Tree, Random Forest
👉 Use cases: Spam detection, churn prediction
🔹 Regression (Numbers)
Linear, Ridge, Lasso
👉 Use cases: Sales forecasting, pricing
🔹 Dimensionality Reduction
PCA, ICA
👉 Use cases: Visualization, noise reduction
🔹 Association Rules
Apriori, FP-Growth
👉 Use cases: Recommendations
🔹 Anomaly Detection
Z-score, Isolation Forest
👉 Use cases: Fraud detection
🔹 Semi-Supervised Learning
Self-Training, Co-Training
🔹 Reinforcement Learning
Q-Learning, Policy Gradient
💡 Key Insight:
Focus on when & why to use an algorithm—not just names.
🚀 Start simple. Experiment. Solve real problems.
April 6, 2026 683 5
