Machine Learning Course

 Learn what unsupervised learning is, how clustering works, and see real-world examples like Google News grouping, DNA data analysis, and customer segmentation. What Is Unsupervised Learning? After supervised learning, the second most widely used type of machine learning is unsupervised learning . Unlike supervised learning—where algorithms are trained on labeled data (inputs X with known outputs Y )—unsupervised learning works with unlabeled data . The algorithm is not given the "right answers." Instead, it must discover patterns, structures, or groupings within the data on its own. How Is It Different from Supervised Learning? Feature Supervised Learning Unsupervised Learning Input Data Labeled (X → Y pairs) Unlabeled (X only) Goal Predict outputs (labels) Find structure, clusters Example Spam detection Customer segmentation Clustering: The Most Common Unsupervised Learning Technique One of the most popular forms of unsupervised learning is clustering , where a...

Meta Description: Discover what supervised learning is, understand the difference between regression and classification algorithms, and see real-world examples like breast cancer detection, spam filtering, and price prediction. What Is Supervised Learning? Supervised learning is a core technique in machine learning (ML) where algorithms learn to map inputs (X) to outputs (Y) using labeled data. The system is trained on examples where the correct answer is already known. Once trained, it can make predictions on new, unseen data. Two major types of supervised learning algorithms are: Regression – Predicts continuous numerical values. Classification – Predicts categories or discrete labels. This post focuses on classification , a powerful method widely used in healthcare, security, finance, and beyond. Classification Algorithms: Predicting Categories, Not Numbers In regression, algorithms predict numbers from infinitely many possibilities (e.g., house prices). Class...