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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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3926 - 3950 of 6,053 Reviews for Supervised Machine Learning: Regression and Classification

By Cuong P C

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Feb 25, 2023

Thanks for the great instruction.

By Long T B

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Dec 28, 2022

Andrew is the best ML instructor.

By 20131A4245 R A

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Dec 25, 2022

Knowledge gained is just Amazing.

By Onwunyi C

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Oct 18, 2022

Wonderful and challenging course

By Debi R

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Sep 29, 2022

it is the course for everyone...

By Meysam M

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Sep 27, 2022

I appreciate it. It was awesome.

By Yugal K

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Aug 16, 2022

Very informative and interactive.

By Julio A L C

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Aug 6, 2022

Lovely course, I learned so much!

By Birhanu G

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Aug 2, 2022

Best course for machine learning.

By Syed A n

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Jul 24, 2022

Very Clear and in depth learning.

By Hardy W

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Jul 21, 2022

pretty straightforward and clear!

By Shruti S

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Oct 31, 2025

Can be made little kore engaging

By Somaditya S

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Jul 19, 2025

very detailed and well explained

By Nalin t

•

Jul 8, 2025

its effective and explained well

By Aymone K

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Jun 23, 2025

Fantastic course and instructor!

By Amrita S

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Jun 15, 2025

so knowledgeful course thank you

By Sakib J

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May 4, 2025

Andrew is a good man. Thank you!

By gattu m

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Jan 20, 2025

Very good course and informative

By RAHUL R

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Nov 6, 2024

First step into the ocean of AI.

By Mohamed Y

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Oct 28, 2024

Thank you Andrew and coursera <3

By Utsyo C

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Oct 20, 2024

Amazingly curated and presented!

By James C A

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Oct 1, 2024

Very well taught in all aspects.

By ISHITA M

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Sep 30, 2024

One of the best Coursera course.

By Samrajya B

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Sep 28, 2024

An amazing course for beginners.

By Prathamesh k

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Aug 1, 2024

well explained by the instructor