Python: Logistic Regression & Supervised ML
Completed by ZAHRAA ALMOHAMED ALI
October 26, 2025
4 hours (approximately)
ZAHRAA ALMOHAMED ALI's account is verified. Coursera certifies their successful completion of Python: Logistic Regression & Supervised ML
What you will learn
Define the lifecycle, objectives, and workflow of a supervised machine learning project using Python and real-world datasets.
Apply exploratory data analysis, data cleaning, and feature engineering techniques to prepare datasets for modeling.
Build supervised machine learning models using Decision Trees and Logistic Regression with essential Python libraries.
Evaluate model performance using confusion matrices and cross-validation to improve reliability and generalization.
Skills you will gain
- Category: Model Evaluation
- Category: Applied Machine Learning
- Category: NumPy
- Category: Logistic Regression
- Category: Statistical Visualization
- Category: Model Deployment
- Category: Scikit Learn (Machine Learning Library)
- Category: Data Analysis
- Category: Feature Engineering
- Category: Data Visualization
- Category: Decision Tree Learning
- Category: Machine Learning Algorithms
