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

4.9
stars
32,084 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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5151 - 5175 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Neel P

•

Jul 25, 2022

Excellent

By Yona H

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Jul 12, 2022

Excellent

By Thành T

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Oct 1, 2025

exellent

By Sadiq A B

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Jul 8, 2025

love it.

By Ron Y

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Jun 18, 2025

woohoooo

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May 25, 2025

The best

By AMINE A

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Apr 7, 2025

just top

By OliviaChen

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Apr 2, 2025

inspring

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Mar 13, 2025

its good

By Suhas Y P

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Feb 10, 2025

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By Amjad A G

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Dec 29, 2024

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By Pawan G

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Dec 8, 2024

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Dec 8, 2024

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Nov 29, 2024

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Nov 17, 2024

its good

By Nicholas W

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Oct 23, 2024

Amazing.

By Prachet P

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Sep 21, 2024

Worthy!!

By 屈杰敏

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Aug 30, 2024

helpful!

By Yazan H

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Aug 1, 2024

Amazing!

By Kumar D

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Apr 26, 2024

The Best

By Karus H

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Mar 15, 2024

Amazing!

By Imrane G

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Feb 3, 2024

the best

By 鍾易達

•

Dec 20, 2023

the best

By Abd a A N

•

Nov 8, 2023

not easy

By 李嘉洋

•

Sep 16, 2023

非常好，浅显易懂