EDIBM · on edX
Machine Learning with Python: A Practical Introduction
Beginner 25 hours English Professional CertificateFREE
What you'll learn
Distinguish supervised and unsupervised learning
Apply regression and classification algorithms
Use clustering and dimensionality reduction
Relate statistical modelling to machine learning
Build a classification prediction model
Create recommender systems
This course includes
25h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
Compare alternatives for Machine Learning with Python: A Practical Introduction
Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
EDedX—(0)
Machine Learning with Python: A Practical Introduction
- Price
- FreeAudit free · IBM certificate available (paid)
- Duration
- 25 hrs
- Level
- Beginner
- Certificate
- Professional
COCoursera4.9(78,000)
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- FreeAudit free · Cert $49/mo
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- Intermediate
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AWS Certified AI Practitioner
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- FreeAudit free · Cert $49/mo
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EDedX4.4(131)
Data Science: Building Machine Learning Models
- Price
- FreeAudit free · HarvardX certificate available ($149)
- Duration
- 24 hrs
- Level
- Beginner
- Certificate
- Professional
Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
SA
Saeed Aghabozorgi
edX instructor
— learners— courses — instructor rating
Taught by Saeed Aghabozorgi, PhD, a Senior Data Scientist at IBM specialising in machine learning and statistical modelling on large datasets.
Requirements
- Recommended: Python basics for data science
Who this course is for
- Python users new to machine learning
- Aspiring data scientists
- Analysts adding ML skills
About this provider
ED
edX
Non-profit online learning platform founded by Harvard and MIT. 45M+ learners worldwide.
4.5 trust score
Frequently asked questions
Yes — you can audit it for free. A verified IBM certificate and a digital skill badge are available for a fee.
Python, with hands-on labs using common ML libraries — a practical complement to R-based or theory-first ML courses.
It's introductory; basic Python (IBM's Python Basics for Data Science) is recommended first.
About five weeks at 4–6 hours per week, self-paced.
IBM's is shorter, applied and Python-based; HarvardX's is statistical and in R. Together they cover both the how and the why.