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
Discover new skills with $120 off courses from industry experts. Save now.
Gain insight into a topic and learn the fundamentals.
3 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
- Data Manipulation
- Applied Machine Learning
- Feature Engineering
- Exploratory Data Analysis
- Scikit Learn (Machine Learning Library)
- Supervised Learning
- Machine Learning
- Statistical Modeling
- Classification And Regression Tree (CART)
- Data Analysis
- Decision Tree Learning
- Pandas (Python Package)
- Machine Learning Algorithms
- Data Cleansing
- NumPy
- Predictive Modeling
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
September 2025
Assessments
6 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Data Analysis
Coursera Project Network
Status: Free Trial
Status: Free TrialEdureka
Status: Free TrialUniversity of Pennsylvania
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
More questions
Financial aid available,