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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
32,190 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

AA

Apr 30, 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.

WB

May 24, 2024

amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work

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3826 - 3850 of 6,066 Reviews for Supervised Machine Learning: Regression and Classification

By Adeel S

•

Nov 20, 2024

One of the best course I ever done.

By Fazliddin S

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Oct 20, 2024

having kernel methods would be nice

By Juan D C

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Sep 16, 2024

Great Intro and practical examples.

By El H A S

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Jul 21, 2024

Really nice course, thanks Andrew^^

By Nikhil R

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Jul 11, 2024

Learnt A Lot......Thanks Andrew sir

By Greeshma J

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Jun 30, 2024

Best beginner friendly ML course...

By Hong D

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Apr 25, 2024

The course layout is easy to follow

By V Z Z

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Mar 25, 2024

Thank you all for this great course

By ABHINAV

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Feb 14, 2024

Great Course to begin with.........

By Ganesan P

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Feb 4, 2024

It was a great learning experience.

By souhaieb e

•

Jan 29, 2024

merci beaucoup pour cette formation

By Josh B

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Jan 23, 2024

Great course for beginners. Thanks.

By Moe A

•

Sep 18, 2023

Amazing teacher Thanks to Mr.Andrew

By k200122 A

•

Aug 28, 2023

Great in-depth course for beginners

By Sara Y

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Aug 16, 2023

It was a great learning experience.

By Talhi I

•

Aug 4, 2023

Amazing, variant and understandful.

By jaa j

•

Jul 31, 2023

So so sooooooo gooooooooooooooddddd

By Umar S

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Jul 28, 2023

it's a amazing content-based course

By Soumik S

•

Jul 25, 2023

It was a great learning experience.

By Sufyan A

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Jul 22, 2023

very helpful and full of knowledge.

By Karan M

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Jul 8, 2023

amazing mentorship by respected sir

By 2085_Sreekant

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May 20, 2023

very easy to understand and helpful

By Upendra R

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May 17, 2023

Very good for clearing fundamental.

By Pouriya M

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Apr 28, 2023

It was just as perfect as expected.

By Hunain A

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Mar 26, 2023

nice but audio quality was not good