🔹 1. Supervised Learning Algorithms

Algorithm Use Case Type
Linear Regression Predicting continuous values (e.g. price) Regression
Logistic Regression Binary classification (e.g. spam or not) Classification
Decision Trees Intuitive rules-based predictions Classification / Regression
Random Forest Ensemble of decision trees for better accuracy Classification / Regression
Support Vector Machines (SVM) High-margin classification Classification
K-Nearest Neighbors (KNN) Classification based on proximity Classification / Regression
Gradient Boosting (e.g., XGBoost, LightGBM) High-performance ensemble method Classification / Regression
Naive Bayes Probabilistic classifier (e.g. text) Classification

🔸 2. Unsupervised Learning Algorithms

Used for pattern detection in unlabeled data.

Algorithm Use Case
K-Means Clustering Grouping data into clusters (e.g. customer segments)
Hierarchical Clustering Building a tree of clusters
Principal Component Analysis (PCA) Dimensionality reduction
Autoencoders Feature compression & anomaly detection
DBSCAN Density-based clustering

🔺 3. Reinforcement Learning Algorithms

Learning through rewards in an interactive environment.

Algorithm Use Case
Q-Learning Learning optimal policies
SARSA On-policy learning
Deep Q Network (DQN) Deep learning + Q-Learning (games, robotics)
Policy Gradient Methods Directly optimizing actions
Proximal Policy Optimization (PPO) Stable policy improvement

🧠 Algorithm Categories Based on Use & Complexity

Category Algorithms Use Cases / Notes
🟢 Beginner-Friendly Linear Regression
K-Nearest Neighbors (KNN)
Decision Trees 		Easy to understand & implement; great for small datasets
🔵 Advanced Techniques XGBoost / LightGBM
Deep Q Networks (DQN)
Deep Neural Networks (DNN) 		High accuracy & performance on large datasets; used in competitions
🟣 Text / NLP Focused Naive Bayes
Recurrent Neural Networks (RNNs)
Transformers (GPT, BERT) 		Natural Language Processing tasks like chatbots, summarization, etc.

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