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Back to Supervised Machine Learning: Regression and Classification

Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
30,957 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning 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 has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

AP

Sep 3, 2022

A​ perfect course for beginners who are seeking to learn machine learning. I want to thank Mr. Andrew Ng and his team for this wonderful course. Thank you so much for providing this quality course.

MA

Jan 27, 2025

I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and Coursera.

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3526 - 3550 of 5,865 Reviews for Supervised Machine Learning: Regression and Classification

By Ricardo C E

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Jul 19, 2022

Very Easy to understand complex concepts.

By MMEHDI A

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Nov 8, 2025

Awesome and completely beginner friendly

By Mukesh K

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Nov 6, 2025

concise, conceptual, to-the-point course

By samah f

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Sep 16, 2025

Very well structured and simple to Learn

By Kunal M

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Aug 13, 2025

Very nice indepth and easy to understand

By Asmi A

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Jul 9, 2025

Andrew Ng explained concepts profoundly!

By BAIJ S

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Jul 9, 2025

Wonderful my knowledge has been enhanced

By Venna S r

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Mar 31, 2025

I am andrew ng absolute the best teacher

By Shivam J

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Mar 28, 2025

Excellent Cource I Learned a Lot From It

By sambhav s

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Jan 2, 2025

Loved the maths part , labs are too good

By ZH W (

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Oct 1, 2024

very nice combination of math and coding

By T T V

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Sep 24, 2024

It is very fruitful. Highly recommended.

By Devendra J

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Jul 5, 2024

the best course for starting to learn ML

By Mario I E D

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Jul 4, 2024

Learning rate was as smooth as possible!

By Shanuka D

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Jun 3, 2024

One of the best courses I've enrolled in

By Shubham T

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Jun 1, 2024

The concept was taught very effectively.

By Josep M

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May 12, 2024

It is an introductory course. A good one

By random u

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Jan 8, 2024

Learned a lot in a short period of time.

By Reza F

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Jan 6, 2024

You are explaining simply! that's great!

By Abubakar A

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Dec 12, 2023

This is really a good experience for me.

By Bibit K

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Nov 18, 2023

Lab and course materials were crazy good

By Jancirubi K

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Nov 4, 2023

Excellent Instructor. Enjoyed learning..

By Yuchen D

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Oct 9, 2023

well-structured, straightforward,patient

By James H

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Sep 14, 2023

Really clear and friendly to beginner!!!

By John F

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Jul 26, 2023

Excellent pace and coverage of material.