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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
32,190 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

AA

Apr 30, 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.

WB

May 24, 2024

amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work

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3851 - 3875 of 6,066 Reviews for Supervised Machine Learning: Regression and Classification

By Hiro

•

Mar 23, 2023

Great beginning to Machine Learning

By Alaa A A A

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Feb 4, 2023

Thanks! words can't really describe

By Ivan S

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Jan 13, 2023

Good Material and good expalnations

By ALOK K S

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Dec 3, 2022

It was phenomenal.

Kudos to the team

By ilma

•

Nov 18, 2022

Concepts are excellently explained.

By Guantai N

•

Nov 6, 2022

Great course and excellent delivery

By Arash M

•

Oct 21, 2022

Great service to mankind for sure!

By Saurabh A

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Sep 7, 2022

Andrew Ng is the best of the best!

By Aryan A P e

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Sep 6, 2022

Easy to follow and quite intuitive

By Amir-Ali M

•

Jul 30, 2022

Phenominal course for entering AI.

By Hasibul H

•

Jul 18, 2022

One of the best course of Coursera

By Xinyue L

•

Jul 15, 2022

A really good high level overview!

By Aditya P

•

Feb 18, 2026

It is just too good, even in 2026.

By Devanath S

•

Jan 19, 2026

Excellent material and explanation

By Sixto M

•

Oct 31, 2025

Excelente el docente y el material

By Sidney H

•

Oct 15, 2025

I need to take Python & Satistics

By ESHITHA K

•

Sep 28, 2025

Very Usefull ML motivated students

By Ho M D

•

Aug 1, 2025

Perfect start for machine learning

By JW

•

Jun 26, 2025

Excellent Machine Learning Course!

By Suhaib V

•

May 30, 2025

I gained many knowledge, Thank You

By Anshuman N

•

Mar 21, 2025

Very Good beginner friendly course

By Ibrahim K

•

Dec 24, 2024

Amazing explanations and breakdown

By Gajendra K .

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Dec 18, 2024

Teaching method is very excellent.

By Bách C V

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Sep 12, 2024

This course is beginner friendly !

By Harihara s R

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Aug 25, 2024

very helpful thanks for the course