Supervised vs. Unsupervised Learning 🤖📊

Machine learning (ML) is broadly categorized into supervised and unsupervised learning, each serving different purposes. Here’s how they differ:

1. Supervised Learning 📚✅

Definition: The model is trained on labeled data, meaning each input has a corresponding correct output.

✅ Goal: Learn a mapping from inputs to outputs and make predictions.
✅ Training Data: Contains input-output pairs (e.g., email → spam or not spam).
✅ Common Use Cases:

• Classification (e.g., spam detection, fraud detection)
• Regression (e.g., predicting house prices, stock market trends)

🔹 Examples of Algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• Neural Networks (Deep Learning)

🔹 Tools: Scikit-learn, TensorFlow, PyTorch

2. Unsupervised Learning 🔍❓

Definition: The model is trained on unlabeled data, meaning it identifies patterns or structures without predefined answers.

✅ Goal: Find hidden patterns, groupings, or relationships in data.
✅ Training Data: Contains only inputs, no explicit labels (e.g., customer data without predefined segments).
✅ Common Use Cases:

• Clustering (e.g., customer segmentation, anomaly detection)
• Dimensionality Reduction (e.g., PCA for feature selection, data compression)

🔹 Examples of Algorithms:

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Autoencoders
• t-SNE

🔹 Tools: Scikit-learn, H2O.ai, Apache Mahout

🔍 Key Differences Summary