Python: Logistic Regression & Supervised ML

Overview

This hands-on course equips learners with the foundational knowledge and practical skills required to build and evaluate supervised machine learning models using Python. Designed around the real-world Titanic dataset, the course walks learners through the complete machine learning pipeline—from project setup and lifecycle understanding to model deployment readiness. In Module 1, learners will define the machine learning project structure, identify essential Python libraries such as NumPy and pandas, and understand the conceptual foundations of algorithms including Decision Trees and Logistic Regression. In Module 2, learners will apply exploratory data analysis techniques, clean and prepare datasets, and construct engineered features. They will also evaluate their models using metrics such as confusion matrices and cross-validation to improve model reliability and generalization. By the end of this course, learners will be able to independently implement supervised learning models on real datasets and interpret results with confidence.

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.