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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
30,502 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 5,778 Reviews for Supervised Machine Learning: Regression and Classification

By Tanvir A S

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Dec 20, 2023

A wonderful course making ML easier.

By Emre G

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Oct 9, 2023

amazing course, had amazing insights

By Seyed K M Z

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Sep 7, 2023

a great course to start learning ML!

By Ekins K

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Aug 4, 2023

Excellent course, beginner friendly.

By Mahan S

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Jul 24, 2023

that was a great and helpful course.

By hossein t

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Jul 6, 2023

andrew ng is really a great teacher.

By Anaj P

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Jun 28, 2023

this course is ideal for ml beginner

By Nawaraj K

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May 31, 2023

Great course and great instructor!!!

By Thomas T

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May 10, 2023

Best ML course available in Coursera

By Sharif M

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May 4, 2023

Very Simple and informative course

By Justin H

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Feb 11, 2023

Andrew Ng == champion ML instruction

By Ruttanan R

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Dec 26, 2022

Andrew Ng is a really great teacher!

By Xiaodie L

•

Nov 3, 2022

Easily understand and useful course!

By Velizar T

•

Oct 9, 2022

Great explanations and lots of fun!

By Jeffrey S

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Aug 29, 2022

Excellent Course, Highly recommended

By Yiğit T

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Aug 17, 2022

Very easy to understand, great job.

By Fateh M

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Jul 31, 2022

Perfect, as expected from Andrew. Ng

By Md. R H

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Jul 10, 2022

Very effective and wonderful course.

By Banriskhem K

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Jul 9, 2022

Programming assignments are too easy

By Gabriel C

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Sep 16, 2025

Excelente curso, muy bien explicado

By Haraprasad S

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Sep 9, 2025

Awesome content and well presented.

By Sergii Z

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Jul 26, 2025

It's a classic course for a reason!

By Govind B S

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Jul 3, 2025

Excellent teaching by Andrew Ng sir

By Roybert A

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Mar 15, 2025

Felicitaciones, excelente material.

By Partha P D

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Jan 12, 2025

Informative and very comprehensive.