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Python: Logistic Regression & Supervised ML

EDUCBA via Coursera

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Overview

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Build a strong foundation in supervised machine learning by learning how to develop, evaluate, and interpret classification models using Python. In this hands-on course, you will work with the real-world Titanic dataset to explore the complete machine learning workflow, from project setup and data preparation to model evaluation and deployment readiness. You will begin by understanding the lifecycle of a supervised machine learning project, defining problem objectives, and using essential Python libraries such as NumPy and pandas. You will also explore core supervised learning algorithms, including Decision Trees and Logistic Regression, to understand how classification models are developed. Next, you will apply exploratory data analysis (EDA), clean and prepare datasets, perform feature engineering, and visualize data using Python libraries. You will then build and evaluate models by splitting datasets, interpreting confusion matrices, and applying cross-validation techniques to improve model reliability and generalization. This course is ideal for learners who want practical experience applying supervised machine learning techniques with Python. By the end of the course, you will be able to prepare data, build supervised learning models, evaluate their performance, and confidently interpret results using a structured machine learning pipeline.

Syllabus

Foundations of Supervised Machine Learning

This module introduces learners to the foundational concepts and workflows involved in building supervised machine learning models using Python. It covers the real-world context of a data science project using the Titanic dataset, including the project lifecycle, problem definition, essential Python libraries for data analysis, and an overview of key algorithms such as Decision Trees and Logistic Regression. Through hands-on exposure, learners gain the practical knowledge required to begin implementing classification models and understand how to prepare and structure their machine learning pipeline.

Data Handling and Model Building

This module focuses on the practical steps involved in preparing data for supervised machine learning models. Learners will explore the process of conducting Exploratory Data Analysis (EDA), managing datasets, performing feature engineering, and visualizing insights using Python libraries such as pandas and seaborn. It further guides learners through the model building process, including dataset splitting, performance evaluation using confusion matrices, and applying cross-validation techniques to enhance model reliability.