Classification Analysis

The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks. By the end of this course, you will be able to: 1. Understand the concept and significance of classification as a supervised learning method. 2. Identify and describe different classifiers, such as KNN, decision tree, support vector machine, naive bayes, and logistic regression. 3. Apply each classifier to perform binary and multiclass classification tasks on diverse datasets. 4. Evaluate the performance of classifiers using appropriate metrics, including accuracy, precision, recall, F1 score, and ROC curves. 5. Select and fine-tune classifiers based on dataset characteristics and learning requirements. Gain practical experience in solving classification problems through guided tutorials and case studies

• Understand classification as a supervised learning method
• Identify, describe, and apply various classifiers to datasets
• Evaluate, select, and fine-tune classifiers based on data characteristics

• Basic familiarity with concepts and terminology
• Willingness to practice through applied exercises or case-based work