good course , some part is typical more statistical part shown, even i have good understanding of ML , so new learner will find little typical. rest tutor voice and language is understandable.
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
Access provided by Planted Detroit
620,747 already enrolled
4.7
(17,987 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
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Applied Machine Learning
- Dimensionality Reduction
- Regression Analysis
- Predictive Modeling
- Supervised Learning
- Classification And Regression Tree (CART)
- Unsupervised Learning
- Statistical Analysis
- Machine Learning Algorithms
- Feature Engineering
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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Learner reviews
4.7
17,987 reviews
- 5 stars
75.89%
- 4 stars
18.63%
- 3 stars
3.45%
- 2 stars
0.99%
- 1 star
1.02%
Showing 3 of 17987
RV
5
Reviewed on Jan 14, 2025
AG
5
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
5
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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