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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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4851 - 4875 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Michel M F

•

Jun 28, 2022

Great course!

By Trang Q K

•

Jun 27, 2022

Great course.

By BlueX C

•

Mar 3, 2026

good course.

By Cəlal İ

•

Dec 11, 2025

great course

By Thumati S

•

Nov 16, 2025

Great course

By Nasir A

•

Jul 23, 2025

great course

By Shashwata R

•

Jul 13, 2025

Great Course

By Premkumar S

•

Jun 29, 2025

very helpful

By Sarah Y

•

Mar 25, 2025

it was great

By Sadananda B

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Mar 17, 2025

VERY USEFULL

By Lakshay D

•

Mar 11, 2025

Very helpful

By FS20EC016

•

Mar 10, 2025

great course

By Pranav P

•

Mar 2, 2025

Go for it!!!

By Lourdu R

•

Feb 25, 2025

Just perfect

By Mayur P

•

Feb 15, 2025

Very Helpful

By Ndubuisi O

•

Feb 6, 2025

Great course

By Monish J

•

Jan 30, 2025

Just amazing

By Amritesu p

•

Jan 29, 2025

best of best

By 박종서

•

Jan 14, 2025

best lecture

By Henry M N

•

Dec 29, 2024

good content

By Abhishek G

•

Dec 22, 2024

Great course

By Bassam S

•

Dec 11, 2024

Perfection !

By Ahmed R

•

Nov 18, 2024

Just perfect

By Chamindu N

•

Nov 16, 2024

Incredible !

By Sbitnev M

•

Nov 7, 2024

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