Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
Access provided by Stanford University
650,822 already enrolled
18,253 reviews
Intermediate level
Recommended experience
Flexible schedule
1 hour to complete
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.
Skills you'll gain
- Python Programming
- Machine Learning
- Regression Analysis
- Logistic Regression
- Unsupervised Learning
- Classification Algorithms
- Supervised Learning
- Decision Tree Learning
- Scikit Learn (Machine Learning Library)
- Predictive Modeling
- Model Evaluation
- Dimensionality Reduction
- Feature Engineering
- Applied Machine Learning
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
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This course is available as part of
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- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from IBM
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Showing 3 of 18253
RN
Reviewed on May 25, 2020
AG
Reviewed on Feb 1, 2020
Quite an informative course, well presented material without being overbearing for newcomers to ML. Highly recommended to everyone with prior CS experience who wants to get into AI/ML workloads.
JT
Reviewed on Apr 17, 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.
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