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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
30,006 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

AD

Nov 23, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

ED

Apr 13, 2025

Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!

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3676 - 3700 of 5,692 Reviews for Supervised Machine Learning: Regression and Classification

By Yousef A

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Feb 26, 2024

very great course so much to learn

By William T

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Dec 19, 2023

Much better vs the old Octave days

By Pruthviraj C

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Dec 4, 2023

Best! can't expect more than this!

By YALALA V V

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Nov 23, 2023

Excellent course and explanation!!

By Tikhon B

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Nov 12, 2023

Amazing and easy to follow course!

By Janine S

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Oct 25, 2023

Very clear teaching style and pace

By Neeraj K

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Sep 25, 2023

Well taught and structured course.

By Mateusz

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Sep 19, 2023

Really well prepared and executed.

By praveen n

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Aug 28, 2023

Easy explanation of complex topics

By Soumya P

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Jul 17, 2023

Good to have initial understanding

By Aryaman P

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Jul 1, 2023

Mind Blowing experience altogether

By Ramsai k p

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Jun 26, 2023

A really good course for beginners

By Shaktiman C

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May 24, 2023

The videos are easy to understand.

By Mradul B

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May 21, 2023

Great course to start from scratch

By Prabhat R

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Mar 29, 2023

Awesome lecture on regularization.

By Yuxi Z

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Mar 1, 2023

This course really helps me a lot!

By Ezedin M

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Jan 28, 2023

An excellent course for beginners.

By Arijit G

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Nov 28, 2022

the course was absolutely awesome.

By Ali A

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Nov 7, 2022

This was a flawless course for me.

By Ankita R

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Oct 17, 2022

Amazing labs and lab assignments!

By Ivanhoe A

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Oct 12, 2022

Excellent course, very insightful

By Oleksii K

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Aug 30, 2022

The best introductory course ever.

By David T

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Jul 3, 2025

A masterclass in Machine Learning

By Sijan T M

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Mar 23, 2025

Its best course i have ever taken

By Shivabhijit S

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Mar 16, 2025

Topics were explained Really well