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 Lok Jagruti University
617,280 already enrolled
4.7
(17,958 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
- Supervised Learning
- Python Programming
- Machine Learning
- Machine Learning Algorithms
- Scikit Learn (Machine Learning Library)
- Classification And Regression Tree (CART)
- Predictive Modeling
- Statistical Analysis
- Applied Machine Learning
- Feature Engineering
- Dimensionality Reduction
- Regression Analysis
- Unsupervised 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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Learner reviews
4.7
17,958 reviews
- 5 stars
75.88%
- 4 stars
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- 3 stars
3.45%
- 2 stars
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Showing 3 of 17958
RN
5
Reviewed on May 25, 2020
RV
5
Reviewed on Jan 14, 2025
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.
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.
