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Module 9
Supervised Learning: Classification Models
1
Understanding Logistic Regression and the Sigmoid Function
Explain logistic regression and the role of the sigmoid function for classification
2
Logistic Regression with scikit-learn
Train and evaluate a logistic regression model using scikit-learn
3
Understanding K-Nearest Neighbors (KNN)
Describe the k-nearest neighbors (KNN) algorithm and its distance-based logic
4
Implementing KNN with scikit-learn
Implement the KNN algorithm for classification using scikit-learn
5
Decision Trees: Predicting with Splits
Describe how decision trees make predictions by splitting data based on features
Decision Trees with Scikit-learn
Train and visualize a decision tree classifier using scikit-learn
7
Ensemble Methods and Random Forests
Explain the concept of ensemble methods and the advantage of using a random forest
Algorithm Performance Comparison
Compare the performance of different classification algorithms on a dataset
Previous module
Supervised Learning: Regression Models
Next module
Unsupervised Learning and Dimensionality Reduction