I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
Machine Learning with Python
Machine Learning with Python
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
Access provided by Alliance University
617,702 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
- Feature Engineering
- Applied Machine Learning
- Machine Learning Algorithms
- Classification And Regression Tree (CART)
- Dimensionality Reduction
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Python Programming
- Predictive Modeling
- Supervised Learning
- Statistical Analysis
- 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
75.87%
- 4 stars
18.64%
- 3 stars
3.45%
- 2 stars
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Showing 3 of 17958
FO
5
Reviewed on Oct 8, 2020
RN
5
Reviewed on May 25, 2020
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!
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.
