What is Classification?
Classification is a supervised learning technique where models are trained to assign input data to predefined categories or classes. It's used for tasks like spam detection, image recognition, and medical diagnosis.
Common Algorithms
Explore popular algorithms like Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and Naive Bayes. Each has its strengths and ideal use cases.
Evaluation Metrics
Learn how to measure the performance of your classification models using metrics such as Accuracy, Precision, Recall, F1-Score, and ROC AUC. Choosing the right metric is crucial.
Feature Engineering
Discover techniques for creating informative features from raw data. Well-engineered features significantly improve model accuracy and generalization capabilities.
Handling Imbalanced Data
Address the challenges of datasets where classes are not equally represented. Techniques like oversampling, undersampling, and SMOTE are key to building robust models.
Practical Implementation
Walk through real-world examples and code snippets using popular libraries like Scikit-learn to build, train, and deploy your own classification models.