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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
31,037 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

FA

May 24, 2023

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

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.

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3951 - 3975 of 5,909 Reviews for Supervised Machine Learning: Regression and Classification

By KY

•

May 2, 2024

best explanation, best teacher

By Abbas M D

•

Apr 12, 2024

This is a really good course .

By sheriff s

•

Mar 17, 2024

Very good course for beginners

By Aaron T

•

Jan 18, 2024

Simply Outstanding. Thank you.

By Vignesh B

•

Dec 30, 2023

Excellent course for beginners

By AAYUSHMAAN C G

•

Dec 23, 2023

Completely an excellent course

By Jorge B P C

•

Oct 27, 2023

The best teacher I've ever had

By WANG, C

•

Sep 3, 2023

This is an incredible tutorial

By Syed T

•

Aug 30, 2023

Wonderful course for beginners

By Rumyana A

•

Aug 29, 2023

Amazing course! Learned a lot.

By Muhammad S

•

Aug 25, 2023

Excellent, especially the labs

By Jatin R

•

Jun 19, 2023

Arigatou gozaimasu , Andrew Ng

By Shreyans J

•

Mar 15, 2023

Mr. Andrew is a great teacher!

By Shames Y

•

Feb 21, 2023

I had fun completing this... S

By Huajie C

•

Feb 4, 2023

Best course I've ever learned.

By Attila K

•

Jan 27, 2023

The course was simply amazing!

By Aman M

•

Dec 15, 2022

Good, I recommend this course.

By naro

•

Oct 30, 2022

I really learn a lot from it!

By muhammad a a

•

Oct 29, 2022

Amazing and so easy to learn.

By JULIUS M

•

Sep 6, 2022

It has been an amazing course

By Zaur s

•

Aug 30, 2022

Excellent starting point on ML

By Happy

•

Aug 14, 2022

Excellent course for Begineers

By Ionut-Alin D

•

Oct 23, 2025

Very useful for my PhD Thesis

By Saprykin A

•

Oct 23, 2025

Thanks, Andrew! Great course.

By Dr S R

•

Jul 4, 2025

Very useful and easy to learn