Supervised vs. Unsupervised Learning in Machine Learning

Supervised learning involves training a model on labeled data, where the algorithm learns to predict the output based on input features and their corresponding labels. Examples of supervised learning algorithms include:

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

Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the algorithm learns to find patterns or structure in the data without explicit feedback. Examples of unsupervised learning algorithms include:

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Association Rule Learning
• t-Distributed Stochastic Neighbor Embedding (t-SNE)

Understanding the difference between supervised and unsupervised learning and knowing the examples of algorithms for each type is crucial for mastering Machine Learning techniques.