Data Mining Course 4 chapter 3 Classification Lecture 4 4

Description:

In this video, we dive into Classification, one of the most important tasks in Data Mining and Machine Learning.

You will learn:
✅ What classification is and why it’s used
✅ The difference between classification and prediction
✅ The classification process: training, testing, and deployment
✅ Popular classification algorithms:
– Decision Trees (ID3, C4.5, CART)
– Naive Bayes
– k-Nearest Neighbor (k-NN)
– Logistic Regression
– Support Vector Machines (SVM)
✅ How to evaluate classification models using accuracy, precision, recall, F1-score, and confusion matrix
✅ Real-world examples: email spam detection, fraud detection, sentiment analysis, and medical diagnosis

Classification is a supervised learning technique that helps us build models capable of predicting categorical outcomes (Yes/No, Spam/Not Spam, Fraud/Legit). By the end of this lecture, you will understand how to build, train, and evaluate a classification model for your own datasets.

📚 Keywords & Tags:
Data Mining, Classification, Machine Learning, Decision Trees, Naive Bayes, k-NN, Support Vector Machine, Supervised Learning, Confusion Matrix, Precision Recall, AI Tutorial, Computer Science Lecture