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

4.9
stars
32,078 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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5051 - 5075 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Mislav P

•

Jul 18, 2022

Fantastic!

By Ibrahim H M A

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Jul 15, 2022

great work

By Sagar K

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Jul 14, 2022

Thank You!

By Quân s

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Feb 27, 2026

very good

By Ikram U

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Dec 23, 2025

Best ever

By Medetbay M

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Dec 17, 2025

thank you

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Oct 30, 2025

very good

By Ahmed M

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Oct 15, 2025

Excellent

By Gulsum

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Oct 10, 2025

Recommend

By Shivam B

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Oct 4, 2025

very nice

By Ahmed W

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Sep 18, 2025

excellent

By Alizon V

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Sep 16, 2025

EXCELENTE

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Sep 15, 2025

Muy bueno

By Sunny J

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Aug 31, 2025

Excellent

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Aug 13, 2025

Very good

By EDGARD A D S

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Aug 6, 2025

muito bom

By AI E

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Aug 3, 2025

Excellent

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Jun 27, 2025

excellent

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Jun 14, 2025

very Good

By Mohammad Z N

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Jun 5, 2025

Excellent

By KUKKADAPU V

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Apr 7, 2025

excellent

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Mar 10, 2025

Excellent

By dhruva s

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Mar 5, 2025

excellent

By Abdelwahed A

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Feb 17, 2025

Excellent

By Eeraj A R

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Feb 16, 2025

loved it!