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
18,654 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:

1201 - 1225 of 3,857 Reviews for Supervised Machine Learning: Regression and Classification

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 Hossein N

Mar 19, 2024

I wholeheartedly appreciate Mr. Andrew Ng for his brilliant and fluent teaching skill. This course was fantastic.

By Vagheesh M K

Dec 30, 2023

The course instructor is the Master of AI Andrew Ng. What more can I say. Perfect Course in all sense of the term

By Kpl A

Dec 2, 2023

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

By 陈瑞麟

Aug 6, 2023

我认为吴老师的授课十分有趣,而且学习的章节内容安排十分具备逻辑性,让我乐在其中。但是我认为对于我这样的代码新手,在实验室部分应该先进行需要学习的代码的认识再进行编写,不要直接给出现成代码,这样能促进学习的效率和引起更高的兴趣。

By Karthik B

Jul 27, 2023

I am grateful to Coursera, Andrew Ng and all the people involved in making this wonderful course available to me.

By Kalgee J

Jul 15, 2023

The course is well-taught, theoretical as well as practical concepts would be far more clear after completing it.

By Javier S H

Jun 18, 2023

Thanks and congratulations for this course. Really nice introduction to regression and classification algorithms!

By Mihir U

May 9, 2023

Explained the underlying working of ML algorithms and teaches the abstractions that are used in the real world...

By Anand J

Feb 24, 2023

This is the best course to learn the first two algorithms of machine learning. Highly reccomended for beginners.

By jelena p

Jan 15, 2023

Great course! I learnt a lot and I would recommend this course to anyone starting out a Machine Learning journey.

By antonin p

Dec 22, 2022

Maybe students could have more to do : the examples are already fully completed and most of the lab are optional.

By Ali R

Nov 29, 2022

This course is one of the best Ml courses in the world. I enjoyed the lessons and also like the teacher very much

By Shubham P

Oct 20, 2022

NICE AND EXCILLENT , HELPED ME LOT TO LEARN THE BASICS OF MACHINE LEARNING AND IMPLIMENTING THE REGRESION MODELS

By Balaji

Sep 8, 2022

My personal favorite is the interactive notebooks which helped me finally grasp on the concept of regularization

By Reza

Aug 16, 2022

I like the full and easy way of teaching that Andrew Ng has. Nothing to say but I've ejoyed attending the class.

By Wisal M

Aug 7, 2022

Well designed and excellently organized contents!

Math part was exquisite, I understood it although I didn't xd.

By Brigitte D

Jul 19, 2022

It's a good starting point for a Machine Learning journey and I help you to start refreshing your Python skills.

By Tunar M (

Feb 7, 2024

The course content is truly incredible. I am sure that it will contribute greatly to my future learning journey.

By 22CH10025 A D

Sep 29, 2023

I got thorough understanding of ML topics and also the practical implementation of algorithms from this course.

By Pranava K M

Sep 3, 2023

Amazing instructor and course design, assignments and labs were very helpful. Thank you for making this course.