Chevron Left
Back to Supervised Machine Learning: Regression and Classification

Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
24,947 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

JM

Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

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.

Filter by:

1451 - 1475 of 4,876 Reviews for Supervised Machine Learning: Regression and Classification

By Paul H

Apr 1, 2023

Great course. Andrew Ng really makes the learning curve easier for such a subject as this. Looking forward to more.

By Yash I

Dec 18, 2022

The course was excellent and enjoyed it.....Thanks so much....(plz add support vector machines(SVMs) to the course)

By Boris S

Oct 11, 2022

I like this cource. I think it is done exactly right for someone who is familiar with basic math and basic python.

By Ying W

Sep 2, 2022

Thanks a million for Andrew and the team for this amazing module! Cannot wait to finish the whole specialization!!!

By Jeff H C

Aug 14, 2022

I loved the course. It was very challenging, but well worth my time. It was a pleasure learning from Professor Ng.

By Mr. C

Aug 12, 2022

Absolutely impressive lectures and supporting materials.

Two thumbs up to the main instructor and the co-workers !!!

By Laís L

Jun 30, 2022

Lectures and exercises are very well explained and easy to follow. Thank you Andrew Ng for this excellente course.

By Fai N m A

Nov 12, 2024

دورة رائعه ومليئة بالمعلومات المفيده اتمنى طرح الكثير من هذه الدورات عن بعد لاتاحة الفرص للجميع بالتعلم والأستفاده

By Kimia N

Sep 30, 2024

very well structured and clearly explained every concept. maybe add some harder questions to the practice quizzes.

By Hir R B

Aug 6, 2024

I love how the algorithms are explained more in an intuitive way. The material and optional labs are very helpful.

By Afrina A

Jul 3, 2024

Such an wonderful course, what helped me most was gaining insights into how those algorithms worked. Truly helpful

By Shaikh A

Jun 23, 2024

lovely course designed by Deeplearning.ai and stanford university , andrew ng is best tutor in this world for ml .

By Roya P

Mar 5, 2024

With this course, I relearned all my ML knowledge much much intuitively. The intuition was a breakthrough for me.

By Lakshmi N R B

Sep 9, 2023

One of the first courses enrolled to in coursera. Awesome experience at Coursera. Thank You Coursera and Andrew Ng

By Tùng N T

Aug 11, 2023

-Simple explains for people who are beginners in Machine Learning.

-Have a lot of examples and codes for practicing

By Kashyap I

Mar 6, 2023

Loved it, one feedback I have is scit learn might have been additionally graded, as that is used in industry a lot

By Jason

Mar 6, 2023

Very well taught. Andrew Ng is articulate and able to break complicated concepts down to easily digestible blocks.

By subarto k g

Feb 23, 2023

I am very much glad to gain knowledge on machine learning. Thanks for giving your support to complete this course.

By Mohammadreza K

Feb 16, 2023

Excellent Job on the lectures and labs and the way the whole problem is broken down into little steps.

It was great

By Miguel G

Jan 24, 2023

Very instructive and practical. It's great to have this course updated with python instead of the old MATLAB code.

By Beshli-ogly A T

Nov 17, 2022

I can not send last tasks,a solved all problem correctly,but the output the same ,can you help or fix this problem

By Mehrdad E

Oct 7, 2022

This course was very useful. My suggestion is to use a more energetic teacher and improve the assignment section.

By Reza R

Aug 31, 2022

I really like Andrew Ng. I learned many great things from him and I find this course very helpful and interesting.

By Raghavendra l

Jul 22, 2022

precise and clear, all the details were very well explained by Andrew. Thanks for providing such wonderful course.

By Kpl A

Sep 20, 2024

Very much understandable and best course on ML to learn, Thankyou Deeplearning.ai , Stanford Online and Andrew NG