This is a fantastic course. Andrew does a great job of covering the fundamentals of machine learning . The focus is on understanding the nuts and bolts of machine learning algorithms as opposed to the practical aspects of conducting an analysis with popular open-source libraries like Scikit-learn. It covers linear regression and classification and, along the way, shows you the basics of feature scaling, feature engineering and regularization. There is some math, but it is presented in a completely accessible way.

My main suggestion for improving this course would be to have more required labs and to do more scaffolding with respect to testing the student's knowledge of key concepts. Some supplemental coding videos may help as well. The labs are infinitely more challenging than the quizzes and students without a coding background and/or knowledge of Python may struggle or have to rely heavily on the hints.

I recently completed Supervised Machine Learning: Regression and Classification by Andrew Ng on Coursera, and it was an excellent experience. The course was very clear and well-structured, making even complex concepts easy to understand. Andrew is truly one of the best teachers—he explains ideas step by step, gives great real-world examples, and makes sure the fundamentals are strong before moving forward. I especially liked how the course balanced theory and practice. The coding exercises were straightforward yet very effective in reinforcing the material. I came away with a solid understanding of regression, classification, and how supervised learning models work in practice. Overall, this course is one of the best introductions to machine learning I’ve taken. I highly recommend it to anyone starting their journey in ML—it’s engaging, practical, and taught by a world-class instructor.

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.

The first course of the Machine Learning Specialization by Andrew Ng has been nothing short of inspiring and enriching for me. I got to learn several beginner ML concepts like regularization, overfitting, bias and variance, z-normalization, approximation, and essential algorithms such as linear regression and regularized regression. The learning process was incredibly smooth and easy to follow. The lectures used simple language without compromising on depth or clarity, which I truly admire. For me, the most valuable learning moments came from the optional and practice labs — they perfectly combined the worlds of coding and math, allowing us to explore both sides together in a meaningful way. In a nutshell, completing this first course was an amazing experience. I'm really looking forward to learning even more in the upcoming ones. Thank you! Samarth Bhatia — India

The Supervised Machine Learning Course on Coursera is taught by Andrew Ng, a leading expert in the field of Machine Learning. The course is designed to provide students with a comprehensive introduction to the key concepts, algorithms, and tools used in supervised learning.

One of the standout features of the course is the programming assignments. These assignments give students hands-on experience implementing the algorithms they learn about in the lectures. The programming assignments are challenging but well-structured and provide detailed feedback to help students improve their coding skills.

Overall, the Supervised Machine Learning Course on Coursera is an excellent resource for anyone who wants to learn about supervised learning. The course is well-structured, the lectures are engaging, and the programming assignments provide valuable hands-on experience.

"Supervised Machine Learning: Regression and Classification" is an outstanding course that provides a solid foundation in machine learning concepts. The course structure is well-organized, with clear explanations and practical examples that help bridge the gap between theory and application. The hands-on coding exercises and real-world datasets make the learning experience engaging and impactful. The instructors do an excellent job of breaking down complex topics into digestible segments, making it accessible for both beginners and those looking to reinforce their knowledge. The emphasis on both regression and classification techniques ensures a comprehensive understanding of supervised learning. Overall, this course is a must for anyone looking to delve into machine learning and build a strong skill set in data science. Highly recommended! 🌟🌟🌟🌟🌟

Andrew Ng is an excellent proffesor, he excell at machine learning, while he is talking to you, you can't avoid thinking "Wow, this guy knows a lot of it". I loved the math in-depth optional sections, because it helps you to truly understand what is behind the scenes in the IA Algoritms. My next goal is Unsupervised and Neural Networks with Andrew. I hope that courses will be success as it was. Therefore i will complete my Online IA Learning courses with Math for Machine learning also taught by Stanford. I really appreciate this opportunity of financial aid to enhance my capabilities. I really really appreciate it a lot because when i finish my roadmap i hope to turn into a scientist in this field, i will do my best to improve human quality life, no matter physical properties, everybody deserves a good pass in this life, i will be in that moment.

"Supervised Machine Learning: Regression and Classification" is an exceptional foundational course that serves as a perfect starting point for anyone interested in machine learning. It is a well-structured, insightful, and practical course that balances theory and hands-on practice effectively. The course makes brilliant use of visualizations. Andrew Ng employs intuitive visual aids to explain key concepts such as cost functions, decision boundaries, and gradient descent. These visualizations make it easier for both beginners and advanced learners to grasp complex topics. If you're just starting in machine learning, this course provides a solid foundation. If you're a programmer (Python, JavaScript, etc.) looking to transition into ML, this course offers a clear and structured approach to help you get started. I love, love, love it! ❤️🔥

I wanted to take a moment to express my sincerest gratitude for the wonderful opportunity you provided by offering courses in sanctioned countries. This gesture truly exemplifies your commitment to global education accessibility.

A special thanks goes out to Andrew Ng for his exceptional teaching in the Machine Learning course. His passion for the subject and clear explanations made the learning experience immensely enjoyable. I can confidently say that my machine learning knowledge has improved significantly.

Coursera's dedication to breaking down barriers and providing quality education worldwide is truly commendable. I am thankful for the chance to expand my skills and knowledge through your platform.

Thank you, Coursera, for making a difference in the lives of learners worldwide and empowering us to reach our full potential.

This course is excellent and it exceeded my expectation.

The explanations provided are top-notch, thanks to the instructor's excellent ability to convey complex concepts with clarity.

Overall the quality of this course is excellent.

However, to further enhance the learning experience, incorporating video tutorials that explain Python libraries like numpy, matplotlib, and scikit-learn would be highly valuable. Instead of solely providing code in the optional lab, these videos would offer hands-on guidance, ensuring a deeper understanding of their practical usage.

Moreover, the inclusion of a mini project, where students can actively solve and code AI problems alongside the instructor, would greatly enhance the learning experience. I highly recommend this course for its clarity and potential for further improvement.

Here's a review tailored to your style: The Supervised Machine Learning: Regression and Classification course, taught by Andrew Ng, is an outstanding learning experience. It masterfully breaks down complex concepts like linear and logistic regression, overfitting, and regularization into digestible and practical lessons. The hands-on exercises are incredibly well-designed, reinforcing theoretical knowledge with real-world applications. Andrew Ng’s teaching style is both engaging and clear, making advanced topics accessible and enjoyable. This course has not only deepened my understanding of machine learning but also strengthened my confidence in applying these techniques effectively. A must for anyone looking to build a solid foundation in supervised machine learning!

This was an amazing course. The lectures were Superb, they somehow gave you all the information you needed without having any background along with going in depth enough for you to actually understand how things really work. The optional labs were amazing They showed you very readable and functional code that implemented all of the more theoretical algorithms we learned in the lectures and it was extremely easy to change the code yourself and experiment which helped you get a much deeper understanding of the material. The tests were well made They were very straightforward if you understood the material but actually tested your knowledge. This was a great course that is easy enough for a beginner but we'll still manage to teach a lot even if you are not a beginner.

This is really a fantastic course as it provides hands-on machine learning experience, but also a lot of intuition as Andrew is so brilliant at explaining complex concepts in very simple and understandable language and visualizations.

It is very friendly to non-math students as well as high school math such as basic linear algebra and calculus may suffice to get a lot of intuition yet without being too overwhelmed by the formality of math.

I also really like the structure of the course, and I now understand very well concepts such as the loss of a single data entry, aggregating losses into an overall cost function, and using the gradient descent algorithm to minimize the cost function to find optimal parameters for learning a curve that fits the input data.

Very simply and wonderfully explained - the contribution of this course is really the way it provides a gentle introduction of concepts that eventually promise to be applicable the same way for far more complex algorithms. Provides a good balance of intuitive understanding and the math behind the concepts.

I do wish the course were a little less gentle and went faster in places, delved into the math a little deeper (e.g., for logistic regression), the intuitiion in places a little deerp (e.g., regularization's impact on mean square cost) -- but, I perfectly understand the difficult tradeoffs that have to be made here to appeal to the broader audience.

Bottom line - Andrew and the others that helped him with this course have done an outstanding job.

Excellent course If you want to learn how Machine Learning systems work and how we check if it is working fine or not, this course is the best.

This course builds the mathematical ground and gives a visual support as well to understand the concepts better. One of the things I appreciated most about the course was the emphasis on understanding the intuition behind the models, rather than just memorizing formulas. This approach made it much easier to comprehend how the models work and how to choose the appropriate model for a given problem.

The course is well-organized and has a great balance between theory and practice. The quizzes and assignments are well-structured, and the feedback provided is informative and helpful.

Thank you, Professor Andrew Ng

This is an excellent introduction - I love Andrew Ng's courses! - it is exceptionally clear in defining terms, concepts and algorithms and steers a very sensibke course with respect to the associated mathematics making it the perfect first course in Machine Learning. Moving the course to python was essential and it is good to see a lot of example notebooks with supplementary material in. I'd recommend students look at Geron's OReilly Book (Hands On Machine Learning ...) afterwards to see more coding examples in the book and associated github repo. One gripe was that you didn't make students do vectorised code for the two programming asignments. I commented out the example code in week 3 asignment and substituted vector code (which runs fast).

I think this course is both suitable for beginners who has only basci programming / ML ideas or for someone who wants a review of what they've learned in school. No matter where you stand right now, you come out as capable of applying the algo on real datasets. The course mostly about the application of regression, classification. No endless details or math theories and focus on the big picture. Very practical! The coding assisngmnets and quizzes are very on point, and literally an application of the algorithm, with step by step guidance. Instructor has made the learning easy! Andrew can explain an algowithm cleraly within 5 minutes, which shows he's knowledge and understands the mindset of learners instead of experts. Thanks again!

This is a great introductory course to Machine Learning. It reaches the fundamentals of Machine Learning, starting from Linear Regression and then showing a variety of techniques to improve our models.

I really liked the way in which everything is explained. Andew Ng has an amazing ability to explain concepts in a didactic and simple way.

Even though knowing calculus is not necessary for necessary for completing and understanding this course, it is greatly recommended to know some calculus in order to better understand what is going on at a mathematical level. I really liked that the course shows the mathematical reasoning behind the learning models.

Overall, this is a great course and I highly recommend it. Can't wait to start Course 2!

I just finished this course, and I have to say it was absolutely fantastic. The concepts were explained in such a clear and straightforward manner, making even the more complex topics easy to grasp. I appreciate how the professor structured the lessons and provided ample examples to ensure understanding. The course material was relevant, well-organized, and really engaging. The professor’s teaching style is truly exceptional—thoughtful, approachable, and passionate about the subject matter. It's easy to see why I'm now a big fan! Every lesson felt like a step forward in my understanding, and I can't wait to apply what I've learned. Highly recommend this course to anyone looking to deepen their knowledge in the subject!

The Supervised Machine Learning: Regression and Classification course by Andrew Ng provides a solid foundation in fundamental ML techniques, covering linear regression, logistic regression, gradient descent, cost functions, and evaluation metrics. It balances theory with hands-on coding exercises in Python, making it beginner-friendly while still offering practical applications like house price prediction and spam detection. The course is well-structured, concise, and easy to follow, though it does not cover deep learning or large-scale real-world projects. Overall, it’s an excellent starting point for anyone new to machine learning, offering clear explanations and practical insights from one of the best AI educators.

Hi,

I already took Andrew NGs "Machine Learning" course a few years ago.

Taking it again (in Python this time) was a great refresher !

Although I understand the need to make the course more accessible I feel like the math was oversimplified at times( standard deviation, probabilities, core math functions).

Moreover I think the course should have covered EDA and feature selection before introducing supervised algorithms.

Finally, I was a bit dissapointed by the scikit learn optionnal lab, I expected more.

Still, I feel like this is the best introduction to machine learning there is.

There is a great balance between theory and practice and I like how Andrew calls upon our intuition.

This is why I give this course 5 stars.

This course provided a strong foundation in two critical machine learning techniques, making it highly beneficial for beginners and those looking to refine their skills in supervised learning. The instructor explained complex topics in an easy-to-understand manner, which was particularly helpful for breaking down algorithms like linear regression and logistic regression.The assessments were thoughtfully crafted to reinforce learning and test practical skills effectively. This course was an excellent starting point for my journey into machine learning. It not only strengthened my understanding of regression and classification but also inspired me to explore more advanced topics in supervised learning.

I highly recommend the 'Machine Learning - Regression and Classification' course to anyone looking to deepen their understanding of these important concepts. The course is expertly designed and delivers a comprehensive overview of both regression and classification techniques in a clear and easy-to-understand manner. The instructor is knowledgeable and passionate, and they do an excellent job of explaining complex topics in a way that is accessible to students of all levels. The course materials and assignments are top-notch and provide plenty of opportunities for hands-on learning. Overall, this is a fantastic course that will leave you well-prepared to apply these concepts to real-world problems.

Perfect course by Andrew and Coursera team. I have been searching for AI and ML courses for last few years. I even subscribed some other courses before but they were not well organized, content was not good, in fact, basic introduction and real applicability was missing. So I did leave them in the middle only. I then found out about Andrew and his expertise in AI and found out this specialization. This course if perfect because: 1. It gives foundation of AI and ML, real uses cases. 2. Andrew explained algorithms in very easy language. 3. Course is very well organized 4. Options labs are really good. (No need to setup anything in your computer for practice) I am honored to learn from Andrew.

I would like to thank Prof. Ng and the overall team for creating a truly incredible course. This is undoubtedly the best course to learn the basics of machine learning.

Prof. Ng is well known about his pedagogical teaching style, so I guess I do not need to say more. But I would like take this opportunity to acknowledge the behind-the-scene members who designed the homework problems and organized the course. The homework problems are very well thought of and they made this course highly effective.

A small comment: I think it will be useful for the curious and math-inclined students if references for some mathematical concepts/derivations are also provided at the end of each lecture notes.