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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
32,127 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

DB

May 30, 2024

Great intro to supervised learning (regression & classification). Clear explanation of sigmoid function and decision thresholds. Could benefit from examples & exploring non-linear boundaries.

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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5851 - 5875 of 6,058 Reviews for Supervised Machine Learning: Regression and Classification

By Ishit A

•

Aug 21, 2023

great explanation for beginners.

By L. M

•

Nov 8, 2025

difficult but very informative.

By Mohit Y

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Jun 9, 2023

I expected more rigrous course.

By WONG, L H K

•

May 23, 2023

No enough mathematical concepts

By Ame S

•

Aug 23, 2024

Nicely explained for beginners

By Kartik

•

Sep 27, 2025

Needed more questions in quiz

By SS K

•

Jun 20, 2025

i like the way of teaching

By M F R

•

Aug 28, 2022

Very well organized course.

By raj t

•

Oct 6, 2024

very helpful for begginers

By Zeyad M A

•

Jul 23, 2024

Lacks end-to-end projects

By Ritil R

•

Jul 6, 2023

teach in a very basic way

By F S

•

Jun 30, 2023

need more code assignment

By Manish P

•

Oct 29, 2025

Good indepth into basics

By Safwen S

•

Jul 29, 2024

lacks practical examples

By Amgad S a h

•

Jul 11, 2023

explain more in the labs

By Jean-Baptiste E

•

Nov 7, 2025

good introduction to IA

By Bharath K

•

Apr 16, 2023

Course content was good

By Umesh P

•

Oct 13, 2025

Useful and Informative

By Luca

•

Mar 9, 2024

at the start too basic

By Samantha H

•

May 2, 2023

good theory grounding

By Phúc T T

•

Aug 23, 2025

Thanh you, Andrew Ng

By El S

•

Feb 12, 2024

Very well explained!

By Foula L

•

Aug 20, 2022

Cours très structuré