Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI
About the Course
libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including
linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration
between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how
to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who h...
Top reviews
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Nov 23, 2022
Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.
ED
Apr 13, 2025
Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!
4626 - 4650 of 5,350 Reviews for Supervised Machine Learning: Regression and Classification
By Josh P
•Oct 1, 2022
Amazing!
By Bardiya R
•Sep 21, 2022
concise
By 杨东海
•Sep 18, 2022
Perfect
By Andrew K
•Aug 6, 2022
So good!
By Anh T N
•Jul 14, 2022
fantasic
By Sujith S
•Apr 28, 2025
amazing
By MOHAMMED A Y B
•Apr 18, 2025
Perfect
By Khaled Z
•Apr 5, 2025
perfect
By Shrey J
•Feb 22, 2025
amazing
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•Feb 7, 2025
Amazing
By Haykal A S
•Jan 30, 2025
amazing
By khan S
•Jan 29, 2025
eminent
By Collins L
•Jan 18, 2025
Awesome
By Shishir B
•Jan 10, 2025
Awesome
By Rishabh M
•Dec 23, 2024
amazing
By Rakia A S
•Nov 16, 2024
4 Stars
By rajvardhan t
•Nov 6, 2024
quality
By abolfazl c
•Nov 3, 2024
perfect
By Srijan R
•Oct 10, 2024
amazing
By Mithun G
•Oct 4, 2024
amazing
By Hridoy D M
•Sep 26, 2024
Helpful
By Emmanuel T
•Aug 25, 2024
Amazing
By Farzaneh J (
•Aug 23, 2024
Awesome
By Robert G
•Aug 8, 2024
perfect
By Gyanendra Y
•Aug 3, 2024
Awesome