Machine Learning with Python
Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively.
Machine Learning with Python is a beginner-level online course from Coursera in Machine Learning. It is structured in 6 modules with 4 stated learning outcomes, over 20h 26m. Learners rate it 4.7/5 from 18.4K ratings on Coursera.
Part of our Machine Learning courses collection, where we compare it against 20 other courses.
Course highlights
| Provider | Coursera |
| Duration | 20h 26m |
| Level | Beginner |
| Mode | Self-paced |
| Language | English |
| Certificate | Yes — shareable certificate |
| Rating | 4.7★ (18.4K reviews) |
| Price | Check price |
About this course
Machine Learning with Python is a beginner-level course offered on Coursera by IBM. It runs for 20 hours 26 minutes and is taught in English. Learners who complete it receive a certificate. The course is self-paced, so you can start and stop according to your schedule.
This course introduces the core tools and techniques used in machine learning with the Python programming language. You will learn to explain key concepts such as supervised and unsupervised learning, and the roles they play in building predictive models. The syllabus walks through linear and logistic regression, methods for building supervised learning models, and approaches to unsupervised learning including clustering and dimensionality reduction.
You will also learn how to evaluate model performance using appropriate metrics, validation strategies, and optimization techniques. A hands-on final project asks you to build and assess end-to-end machine learning solutions on real-world datasets, using scikit-learn and other Python libraries.
By the end of the course you will be able to apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction to data sets. You will know how to evaluate model performance with metrics like accuracy and precision, and how to validate models using train-test splits and cross-validation. The course also covers how to optimize model performance and build complete machine learning pipelines.
This course is designed for beginners who already have a working knowledge of Python, including data analysis and visualization techniques. A high school-level understanding of mathematics is expected. If you are new to Python, the course assumes you can write basic scripts and use libraries like pandas for data manipulation.
The course does not cover deep learning frameworks such as TensorFlow or PyTorch in depth, nor does it teach advanced statistical modelling. Learners who want to specialize in neural networks or large-scale production deployment may want to look at the alternatives listed on the Machine Learning courses page, such as the Python for Data Science and Machine Learning Bootcamp on Udemy, which includes more extensive coverage of these topics.
Enroll for free and audit the materials, or pay for the certificate experience to earn a credential that can be added to your LinkedIn profile.
What you’ll learn
Level and time commitment
- A working knowledge of Python, along with data analysis and visualization techniques, and at least a high school-level understanding of mathematics.
Skills you’ll gain
What this course covers
6 modulesThe 6-part outline Coursera publishes for this course, across 20h 26m.
Module 1 · Introduction to Machine Learning
Module 2 · Linear and Logistic Regression
Module 3 · Building Supervised Learning Models
Module 4 · Building Unsupervised Learning Models
Module 5 · Evaluating and Validating Machine Learning Models
Module 6 · Final Project and Exam
What learners rate it
Rated 4.7/5 by 18.4K learners on Coursera (checked 10 Aug 2026).
This rating is collected by Coursera from its own enrolled learners. We reproduce it as reported and do not accept paid or incentivised reviews. See it on Coursera →
Compiled by the CoursesGlobal editorial desk. Fees, ratings, duration and certificate details for the 1 course shown are read directly from Coursera course pages — we never estimate a price or a rating. Last verified 10 August 2026.
How we compare and rank courses · Our review policy · Affiliate disclosure
Compare with alternatives
Same topic, different trade-offs — here's who runs each one and what it's best at.
| Course | Provider | Rating | Price | Duration | Why pick this one |
|---|---|---|---|---|---|
| Machine Learning with Python This |
 Coursera
|
4.7★ | Check price | 20h 26m | Quickest to finish (20h 26m) |
| Machine Learning |
Coursera
|
4.9★ | Check price | 2 months | Highest rated (4.9★ from 39.2K reviews) |
| Machine Learning: Classification |
Coursera
|
4.7★ | Check price | 21h 25m | Beginner-friendly · 21h 25m |
| Machine Learning, Data Science & AI Engineering with Python |
 Udemy
|
4.6★ | Free | Free — and still gives a certificate |
Where Machine Learning leads
Roles we map to Machine Learning on CoursesGlobal. This is our own mapping of subject to job, not a placement claim by Coursera.
Browse every course we track for one of these — courses for data scientists.
Common questions
How much does Machine Learning with Python cost?
Coursera does not publish a price we can read on the course page, so we do not show one. Check the fee on the provider site — we would rather say nothing than quote a number we did not collect.
Does Machine Learning with Python come with a certificate?
Yes — Coursera lists a certificate on completion. It is a course certificate, not a formal qualification or university credit.
How long does Machine Learning with Python take?
Coursera lists it at 20h 26m. It is self-paced, so that is the volume of material rather than a deadline — how long it actually takes depends on the hours you put in each week.
Is Machine Learning with Python suitable for beginners?
Yes. Coursera lists it at Beginner level, so no prior experience in the subject is assumed.
What does Machine Learning with Python cover?
It is organised into 6 modules, starting with Introduction to Machine Learning and going on to Linear and Logistic Regression, Building Supervised Learning Models, Building Unsupervised Learning Models, Evaluating and Validating Machine Learning Models, among others. The full outline is on this page.