Learning Track

Supervised Learning - Classification

Classification is a supervised learning task where models predict categorical labels, such as spam/not spam or disease/healthy. Models learn decision boundaries from labeled examples to categorize new data. This section covers essential classification algorithms, from simple logistic regression to more complex methods like SVM, plus a practical churn prediction project.

6 Lessons

1 Hours

1 Subtopic

Subtopics

Classification Algorithms

Classification algorithms are supervised learning methods used to categorize data into predefined classes. They analyze patterns in labeled data to make accurate predictions for new inputs. Popular techniques like Logistic Regression, Decision Trees, KNN, SVM, and Naive Bayes help solve tasks such as spam detection, customer churn prediction, and medical diagnosis with high accuracy.

6 lessons

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