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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
30,836 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: 鈥� Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. 鈥� 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鈥檚 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鈥檙e 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

MA

Jan 28, 2025

I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and 糖心vlog官网观看.

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.

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3901 - 3925 of 5,842 Reviews for Supervised Machine Learning: Regression and Classification

By Sayak H

鈥�

Oct 6, 2022

excellent and easy explanation

By gautam g

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Jul 23, 2022

Excellent course for beginner.

By PhucVTHE161744

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Jul 13, 2022

Super useful for beginner in AI

By Haouchine Y

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Jan 5, 2025

Excellent course for beginners

By Syed W B U

鈥�

Dec 16, 2024

Nice course start is difficult

By Amir K

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Sep 11, 2024

Excellent course for beginners

By Nima R

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Aug 12, 2024

It's good course for beginners

By Roaa M

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Aug 5, 2024

Very good, fun and very useful

By Chakradhar S

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Jul 26, 2024

excellent profound and amazing

By HASANCAN A

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Jul 15, 2024

Excellent and perfect course .

By Arshdeep K

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Jun 25, 2024

Everything was well explained.

By RATHOD Y A

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Jun 12, 2024

it is a nice and very helpfull

By KY

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May 3, 2024

best explanation, best teacher

By Abbas M D

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Apr 13, 2024

This is a really good course .

By SHERIFF A F S

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Mar 18, 2024

Very good course for beginners

By Aaron T

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Jan 19, 2024

Simply Outstanding. Thank you.

By Vignesh B

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Dec 31, 2023

Excellent course for beginners

By AAYUSHMAAN C G

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Dec 23, 2023

Completely an excellent course

By Jorge B P C

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Oct 28, 2023

The best teacher I've ever had

By WANG, C

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Sep 4, 2023

This is an incredible tutorial

By Syed T

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Aug 30, 2023

Wonderful course for beginners

By Rumyana A

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Aug 29, 2023

Amazing course! Learned a lot.

By Muhammad S

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Aug 26, 2023

Excellent, especially the labs

By Jatin R

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Jun 20, 2023

Arigatou gozaimasu , Andrew Ng

By Shreyans J

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Mar 16, 2023

Mr. Andrew is a great teacher!