Thank you Coursera & IBM for offering such a wonderful career-oriented course. Thank you very much Dr SAEED AGHABOZORGI and Dr Joseph Santarcangelo for providing the amazing learning Journey.
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
546,244 already enrolled
Included with 
4.7
(17,312 reviews)
Intermediate level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from IBM
Explore more from Machine Learning
Status: Free Trial
Status: Free TrialArizona State University
Status: Free TrialUniversity of Michigan
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."
Learner reviews
4.7
17,312 reviews
- 5 stars
75.98%
- 4 stars
18.62%
- 3 stars
3.39%
- 2 stars
0.97%
- 1 star
1.01%
Showing 3 of 17312
IK
5
Reviewed on Dec 14, 2022
JT
5
Reviewed on Apr 18, 2020
This course was a great taster for machine learning techniques. My only recommendation would be to add more explanation on tuning techniques for models and cover more of the supporting mathematics.
MJ
5
Reviewed on Jun 4, 2020
In peer graded assignments, if someone is grading any peer below passing criteria then it must be compulsory to let the learner know his mistakes or shortcomings because of which he does not graded.
New to Machine Learning? Start here.
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
Python’s popularity in machine learning stems from its simplicity, readability, and extensive libraries like TensorFlow, PyTorch, and scikit-learn, which streamline complex ML tasks. Its active community and ease of integration with other languages and tools also make Python an ideal choice for ML.
Machine learning engineers use Python to develop algorithms, preprocess data, train models, and analyze results. With Python’s rich libraries and frameworks, they can experiment with various models, optimize performance, and deploy applications efficiently.
Python offers a wide range of ML libraries, is beginner-friendly, and has great support for data visualization and model interpretation. It also supports rapid prototyping, making it easier to test and refine models compared to other languages like C++ or Java.
More questions
Financial aid available,