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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
30,880 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

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.

AA

Apr 29, 2023

Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.

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3651 - 3675 of 5,848 Reviews for Supervised Machine Learning: Regression and Classification

By Aryan V

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Jun 1, 2023

Very informative and engaging course.

By Aaryan D

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May 25, 2023

An awesome intro to Machine Learning!

By Willian M

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May 6, 2023

Amazing training, highly recommended.

By yufei

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Mar 30, 2023

helpful! A great thanks to Andrew NG!

By Arthur B S

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Mar 20, 2023

Ótimo curso para quem está começando.

By Samuel G

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Dec 26, 2022

Thank you, this is an amazing course.

By Nikhil T

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Nov 20, 2022

easy to learn with the help of videos

By Rene A D P

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Oct 10, 2022

Incredible resource for learning ai.

By Ali I

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Sep 28, 2022

Thanks alot for your fruitful coures.

By Talha K

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Sep 7, 2022

Concise. Efficient. Beginner Friendly

By Mohamed O

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Aug 25, 2022

very good course, and well organized.

By Kishore I

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Aug 15, 2022

Excellent introductory course on ML.

By emirhan e

•

Aug 7, 2022

Thanks for this precious oppurtinity.

By Carlo D

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Jul 26, 2022

best intro course to machine learning

By Anush R A

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Jul 12, 2022

best course to learn machine learning

By Daniel A

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Jul 11, 2022

Course very well executed. Thank you.

By Sayed M

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Jul 10, 2022

Thank you all for this amazing Course

By 黄金

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Jul 6, 2022

The lab part of the course is amazing

By Yuhao W

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Jul 4, 2022

better than before, with python coded

By Babacar M

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Jul 1, 2022

Great professor and exellent content!

By Henrik S

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Jun 27, 2022

It was everything I wanted it to be!

By Abdalla H

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Oct 22, 2025

Perfect machine learning explanation

By Tanvir A F

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Aug 14, 2025

Very helpful to understand in depth.

By Alessandro D

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Jul 20, 2025

Clear explanations, very useful Labs

By HARSH S

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Jul 6, 2025

It was amazing ... really enjoyed it