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.



Python: Logistic Regression & Supervised ML
This course is part of Python for Data Science: Real Projects & Analytics Specialization

Instructor: EDUCBA
Included with
Skills you'll gain
- Data Analysis
- Scikit Learn (Machine Learning Library)
- Pandas (Python Package)
- Classification And Regression Tree (CART)
- Machine Learning
- Statistical Modeling
- Exploratory Data Analysis
- Feature Engineering
- Machine Learning Algorithms
- Data Cleansing
- Predictive Modeling
- Supervised Learning
- Data Manipulation
- Applied Machine Learning
- Decision Tree Learning
- NumPy
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September 2025
6 assignments
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There are 2 modules in this course
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.
What's included
6 videos3 assignments
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.
What's included
8 videos3 assignments
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