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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
27,808 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

ED

Apr 14, 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!!

AD

Nov 24, 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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3451 - 3475 of 5,329 Reviews for Supervised Machine Learning: Regression and Classification

By Ivan S

•

Jan 12, 2023

Good Material and good expalnations

By ALOK K S

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Dec 2, 2022

It was phenomenal.

Kudos to the team

By ilma

•

Nov 18, 2022

Concepts are excellently explained.

By Guantai N

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Nov 6, 2022

Great course and excellent delivery

By Arash M

•

Oct 20, 2022

Great service to mankind for sure!

By Saurabh A

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Sep 7, 2022

Andrew Ng is the best of the best!

By Aryan A P e

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Sep 6, 2022

Easy to follow and quite intuitive

By Amir-Ali M

•

Jul 30, 2022

Phenominal course for entering AI.

By Hasibul H

•

Jul 18, 2022

One of the best course of Coursera

By Xinyue L

•

Jul 14, 2022

A really good high level overview!

By Anshuman N

•

Mar 21, 2025

Very Good beginner friendly course

By Ibrahim K

•

Dec 24, 2024

Amazing explanations and breakdown

By Gajendra K .

•

Dec 18, 2024

Teaching method is very excellent.

By Bách C V

•

Sep 12, 2024

This course is beginner friendly !

By Harihara s R

•

Aug 25, 2024

very helpful thanks for the course

By pritam d

•

Aug 17, 2024

Great course. I have learnt a lot.

By Abigail L

•

Jul 28, 2024

老师讲得非常好，有趣生动，对我学习机器学习入门帮助真的很大，受益匪浅

By Flávio N

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Jun 20, 2024

Excelente conteúdo, otima didatica

By Saagar T

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May 29, 2024

Perfectly paced and soft lectures.

By Mayank G

•

May 19, 2024

Thanks For Delivering Good Content

By Robert P

•

May 16, 2024

A good start for my learning plan.

By Yousef A

•

Feb 26, 2024

very great course so much to learn

By Wai H T

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Dec 20, 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!!