Global Certificate in Classification Analysis for Competitive Edge

• Introduction to Classification Analysis: Understanding the basics, concepts, and techniques of classification analysis.
• Data Preprocessing: Cleaning, transforming, and preparing data for classification analysis.
• Feature Selection and Engineering: Identifying and creating relevant features to improve classification performance.
• Supervised Learning Algorithms: In-depth study of popular algorithms like Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines.
• Unsupervised Learning Algorithms: Exploring clustering techniques for classification analysis.
• Model Evaluation and Validation: Techniques for assessing and validating classification models.
• Ensemble Methods: Boosting, bagging, and stacking techniques for improving classification accuracy.
• Real-world Applications: Understanding how classification analysis is applied in various industries and scenarios for a competitive edge.
• Ethics and Bias in Classification Analysis: Addressing ethical concerns, potential biases, and fairness in classification models.