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Classification Analysis

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  • Introduction to Classification
    • This week provides an overview of classification as a supervised learning method. You will also learn the K-Nearest Neighbors (KNN) algorithm, understanding its principles and applications in classification tasks.
  • Decision Tree Classification
    • This week you will explore the Decision Tree algorithm, learning its structure, construction, and applications in classification problems.
  • Support Vector Machine Classification
    • This week focuses on the Support Vector Machine (SVM) algorithm, where you will grasp its principles and how it is used for classification.
  • Naïve Bayes and Logistic Regression
    • This week will delve into two essential classifiers: Naive Bayes and Logistic Regression. You will gain insights into their assumptions, strengths, and applications.
  • Classification Evaluation
    • This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.
  • Case Study
    • In this final week, you will apply the knowledge and techniques learned throughout the course to solve a real-world classification problem through a comprehensive case study.