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

4.9
stars
23,866 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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4326 - 4350 of 4,719 Reviews for Supervised Machine Learning: Regression and Classification

By Ellen C

•

Feb 20, 2024

I really like this course. Though for more in depth learning how to apply logistic and linear regression i would recommend Andrew NG older course also still available on coursera. It goes more in depth of how to evaluate a model when implementen and comparing them. Anyway thanks to all the contributors to making this available.

By Nicholas G

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Jan 23, 2024

Consolidates all of the essential beginner information for types of regression and gradient descent. Worth it for busier professionals. Wish there were more hands-on "throw you into the pool and let you swim" types of labs, everything is very hand-held. That is what other resources and personal projects are for, it seems.

By Keegan D

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Jan 22, 2024

Was a great course and the visual representations really helped a lot in understanding key concepts, my only suggestion would be a little more depth in coding know how and video explanation of it than just notebooks to read from. Overall a great course to learn in-depth about regression and classification from ground up.

By mohammad k

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Sep 11, 2023

This first part of the course is probably suits a freshman student in engineering who has limited knowledge of numerical methods or computer programming. While I hold great regard for Mr. Ng, I find the pace of the course to be somewhat sluggish, and the depth of the material covered to be modest.

By Praveena C

•

Jul 19, 2023

Overall, the course was remarkably insightful and presented in a way that made it easy to follow. In the future, I think it would be beneficial for the lessons to delve deeper into the programming aspects of machine learning, as this area demanded a lot more independent research and self-learning.

By Edouard T

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Oct 22, 2024

The course is great and offers a wealth of details, both theoretical and practical. However, as a first course in machine learning, the coding in some labs could be simplified. The labs could have been designed using simpler code, particularly by utilizing NumPy instead of looping over vectors.

By Nyan

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Oct 30, 2022

This is a really really great course which provides the fundamentals of Supervised Machine Learning and also math concepts used in Regression and Classification. Ones who don't have basic math knowledge and basic python programming knowledge will face some difficulites in this course I guess.

By Vishal R

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Jun 30, 2023

It was really insightful, learned about all the theoretical as well as some practical knowledge, Andrew sir's way of teaching is definitely the best way to teach something this challenging, really loved the course, Will surely recommend to someone who's starting out on Machine learning.

By Reza N

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Jun 21, 2023

This course was very helpful for me to understand the mathematics and intuition behind those machine learning algorithms. However, I believe the assignments could be slightly more challenging and demanding, and it would be great to have a small project as a culmination of the course.

By Brian R

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Jul 7, 2022

Amazing coverage of Linear and Logistic regression! There is a significant attempt to lower the math bar for this course, which is appreciated and admirable; but then the labs require implementing equations in code, which requires some focus be paid to that portion of the learning.

By Carlos L

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Nov 5, 2022

Lectures and assignments are well very curated, but assignments are far too easy and encourage explicit imperative programming (for loops and mutated state) rather than vectorisation, which goes against the taught material. Very odd - that's the reason for the 4 stars only.

By John e C

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Aug 19, 2024

Great course. If you would improve something I had a little hard time completing the test's because the code implementations were different to the ones seen in labs. the name of the varbiales changed and some thing's changed so I think this could be difficult for beginners

By Hariom S

•

Jan 1, 2023

It was my first specialization which I completed from Coursera and it was worth my time. The knowledge shared by Andrew Ng is just incredible and I enjoyed the course a lot. Thank you Coursera for making such courses available for us and financial aid was also very useful.

By Anshuman S

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Jul 9, 2024

One of the best Courses on the internet on ML, I am thankful to this course for existing. But if I had a single dislike was that, there was not much hands-on practical practice. Its more theoretical. I would have loved for Mr. Andrew NG to teach us code side by side too.

By Isabelle S

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May 18, 2023

Great course for mathematical fundamentals of Machine Learning. However, I would like the optional labs (that introduce and describe the necessary Python code) to be better explained and walked through. It is hard for me to understand how the passage from concept to code

By Pranjul M

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Aug 4, 2024

course is extremely awesome but there is just one problem that if this course can be audited for free, then there must be some option to mark it as completed without issuing any certification, so that it won't be a part of My Learning section as it irritates me somehow.

By Drew D

•

Dec 25, 2022

wonderful course, and this updated version is much better than the 2018 version which I took. The key thing I found is that I needed to know more Python to pass each weeks ending lab before you could go onto the next week lectures, so more Python knowledge is helpful.

By Diego M

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Nov 24, 2023

Simply magnificent. The course does an excellent job in teaching the fundamentals of regression and classification. However, it would be amazing if the course went into more depth into some of the underlying principles used, such as MLE in logistic linear regression.

By peternunez_

•

Jun 25, 2023

Very good, intuitive teaching.

I would have prefered if more teaching/videos were dedicated to the Python implementations and more rigorous explanations of the concepts. However, I'm satisfied with what I've learned as I feel as if I'm now able to apply the skills.

By CHE 8 C A V K D

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Nov 28, 2022

I feel the course is great but the lectures only focused on theoretical aspect,I believe adding few coding solving or error rectification lessons might increase the quality of course as just looking at optional labs and so the coding assignments felt too difficult

By Steve M

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Jul 25, 2022

Andrew Ng is a great teacher. I took his original Octave-based Machine Learning class when it came out years ago. I've been waiting for the update in Python. I have to say, this class is not as hard or math-intensive as the original, but so far, it's been great.

By Bestman e E

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Jan 22, 2023

A very interactive course that makes learning interesting. The Instructor has a warm disposition that puts the student at ease while keeping focused on the subject. The course would be enriched with the inclusion of interactive sessions or active student fora.

By Gravin P

•

Nov 22, 2022

I really had a good experience learning new concepts of Machine Learning in this course, this course was containing all the necessary things such as thoretical knowledge, quiz's and practical knowledge in the form of optional labs and programming assignment.

By Aditya S

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Jul 16, 2023

Simply Marvelous, The theory presentation is very engaging although I feel that Lab assignments are not properly explained and sometimes it is not clear that what the statement demands. Discussions of Assignments can also be added so it is clear afterwards.

By Argha

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Oct 2, 2023

If they somehow managed to put thorough Practical hands-on hand-coding videos, it would be 5/5, but now it's a 4/5 just because I don't feel confident after completing the course. But since I got it from financial I don't have anything to complain about.