Firstly, all the materials in the learning sessions are consistently produced at a very high quality. All concepts are clearly explained, recapped in various videos, and reinforced through quizzes in the video and assessment labs. Furthermore, there are many honest advice to students about the real practice of machine learning. Moreover, there are many interactive lab exercises that 100% support learning and assessment. Finally, I also like the interview section between Andrew Ng and Fei Fei which gives valuable insight into career advice and machine learning research directions. Overall, 100% satisfaction.

The mathematics is explained in detail, it is true you don't need much mathematical knowledge, pre-calculus knowledge is just fine and helps with intuition, otherwise, you are taken care of with everything explained in detail. The quizzes are very helpful in checking whether you understood the concepts. I loved the labs because there was a lab for each section which gave me hands-on practice, seeing exactly what was going on and learning to apply the concepts. I am extremely grateful for the opportunity to have all this knowledge available to me across the world, this is a great course, and I loved it.

Course of exceptional level of explanation both in theory and from the point of view of labs that allow testing of theory with very useful practical examples. Thanks to Professor Ng for his ability to explain concepts that are not always easy, yet always with a positive disposition and attitude. The labs are extremely meaningful, especially if one gets used to solving problems on one's own, to understand where something might not have been understood. I finished the course with top marks in both the theory part and labs. Thanks to all the people who provided their skills and time to create this course.

The education given in the program was one of the rare moments in my life where I felt that I had really learned something. Although some things are offered optionally in this program, it progresses in a very comprehensive and instructive way. In addition, it is not only focused on completing the course, but also has a developer feature about machine learning. The library and tools that are not actively used in the course but used by today's engineers and researchers are also mentioned in the program. I would like to thank you for this effort, your high level of teaching and your kindness. Best wishes.

Exceptionally detailed and compact at the same time. After completing the course, I can confidently say that I am able to understand the basics of supervised learning completely, how to train a model and how to fine tune them. I had completed this course in 2017, and have started again inn 2023 to go through the course in python and know about the latest changes that have happened since the update to the course. But, this course has been made much better (it was already excellent at that time), and I cannot recommend it enough to all the people who had also completed the course before the update.

I recently completed the "Supervised Machine Learning: Regression and Classification" course on Coursera, and I can confidently say that it exceeded my expectations in every way. This course is an absolute gem for anyone looking to delve into the world of machine learning.

The course content is comprehensive, well-structured, and beautifully explained. From the fundamentals of regression and classification to more advanced topics, each concept is presented in a clear and concise manner. The practical examples and real-world applications make it easy to grasp even for someone new to the field.

As a Django Developer interested in AI field ! This Course has many aspects that should make you encourage to take it in deep 1- It is friendly for beginners who has good knowledge of math 2- It is straightforward and easy to understand the core of human think and the relation between math and Computer Science (Especially AI ) 3- Prof. Andrew Ng without doubt is the most accurate teacher I have ever seen before He used the terms in its right places as well as he used the alternative concepts to bring us a full view . Prof. Andrew and his Team Big Thanks for teaching us !

Enrolling in this course was an exceptional decision. Instructor adeptly demystified complex concepts such as linear regression and logistic regression, providing an engaging and accessible learning experience. The practical labs, enhanced by insightful visualizations, solidified my understanding of these techniques. The course's well-structured quizzes and assignments offered valuable assessments of my progress. In essence, this course is an invaluable resource for anyone seeking a comprehensive understanding of supervised machine learning, making it highly recommended

One of the best online courses available is a course focused on mathematical concepts. This course stands out because it offers highly valuable practice classes in addition to comprehensive content. The course is designed to be accessible and easy to understand, using simple English to explain complex mathematical ideas. It provides a strong foundation in mathematical principles and offers practical applications to enhance learning. Overall, this course is highly recommended for individuals seeking to improve their mathematical skills in an online learning environment

This course did an excellent job of explaining the fundamental concepts behind these important techniques in machine learning. I learned a lot about how these algorithms work and their practical applications in real-world problems.For anyone looking to deepen their understanding of regression and classification, I highly recommend this course. It's a great way to gain a solid foundation in these important techniques, and I feel much more confident in my ability to use them effectively in my work. Thanks to Andrew Ng for offering such a great learning experience!

This is such a solid foundation for ML and AI that this course should be included in high school education (at least as an AP class). For someone who has not been close to solving math problems for over two decades, Andrew's articulation of the content with ease, made me recollect (forgotten long back) concepts that I learned during my pre-university and Engineering days. Will definitely continue with related content and strongly recommend this as a basis for any aspiring AI/ML engineer or people who wants to know - how the damn thing works behind the scene!

What's incredible about this course is that it focuses very narrowly on the most important building blocks of ML, which might almost seem trivial to many, but in fact can speed up your learning of more complex models since you get a chance to work out by hand each and every component of the cost function, loss function, gradient descent and finally the regularisation for both logistic and linear regression models. This course helps you build a very strong intuition for modeling and its at just the right level of complexity without any unnecessary details!

"I believe that it would be more effective to design tests and practice exercises that are challenging and complex. Just like how gold is highly valued because of its rarity and difficulty to obtain, the Certificate becomes more valuable and rewarding when we are faced with challenges that push us outside of our comfort zone. By providing more difficult tasks, we can encourage students to develop their problem-solving skills, critical thinking abilities, and resilience. Ultimately, this can help them to become better learners and more capable individuals."

The "Supervised Machine Learning: Regression and Classification" course on Coursera is a great introduction to key concepts in machine learning. It covers essential topics like regression models, classification techniques, and model evaluation. The content is clear and engaging, with practical coding exercises that help reinforce learning. By the end of the course, you'll have the skills to apply supervised learning techniques to real-world problems. It's a perfect starting point for anyone looking to dive into machine learning. Highly recommended!

This is a really good introduction to ML principles. With concise explanations of important ideas like logistic and linear regression, and overfitting, the course is designed to be easily understood by even the most inexperienced students. The theory is made easier to understand by using examples from everyday life and the entertaining teaching style of Andrew. Your comprehension is reinforced by the hands on exercises that use Jupyter notebooks and Python. For anyone wishing to begin working with machine learning, this course is excellent overall!

"Supervised Machine Learning: Regression and Classification" by Stanford and DeepLearning AI by Andrew Ng is an absolute game-changer! 🚀 This course seriously helped me build a solid foundation and get the hang of machine learning, all without drowning in math. It's like they turned complex concepts into bite-sized nuggets of knowledge. Plus, Andrew Ng's teaching style is on point! 🙌 If you're serious about leveling up your ML game, give this course a shot. Trust me, you won't regret it! 💯 #MachineLearning #Stanford #AndrewNg #LearningMadeEasy

It was an amazing course from the beginning till the end. Especially for a beginner who doesn't know anything about this field and want to learn about it and explore this vast field, this course helped me a lot in learning many new things and a desire to get ahead in this field. Also, in the starting of this course, one can think or feel that they need to know python for this course, or numpy or pandas but trust me you really don't, u can freely start this course without any prerequisites and learn later after this course based on your interest.

This course was very challenging to me, but Andrew Ng is a great teacher. I took this course because I wanted to finish some of my own personal NLP (natural language processing) projects that have been languishing for the past couple of years. I augmented the material in this course with Speech and Language Processing text by Dan Jurafsky. The optional labs are super helpful, and I did all of them. I worked through everything, including watching the interview (at the very end) with Fei Fei Li. Very inspiring and thought provoking. Thank you!

Starts off easy and then gets a bit more challenging. I enjoyed it. A couple feedback points:

- More questions throughout the videos might be helpful. - I'm not sure the labs should be designated optional, since the final labs expect you to write some code.

- It would be nice if there was more coverage on how to do feature engineering (ie. how do you know when to map original features to higher dimensions and orders? I feel like that might be one of the missing links to actually try to "do" machine learning on some practice datasets.

I thoroughly enjoyed the course and gained a wealth of knowledge from it. It is worth noting that while the free enrollment provided valuable insights, the absence of coding assignments limited my ability to fully immerse myself in the material. However, upon enrolling in the paid course program, I was pleased to find that it did include coding assignments, which greatly enhanced my learning experience. As someone who values hands-on experience, the coding component was crucial for solidifying my understanding of the subject matter.

on the final lab, exercises 4 and 5 were extremely confusing when they tried to add the "fill in the blank" style for you to finish the code. I was confusing because the hints were not formatted in the same way at all so it was very frustrating and actually hindered my understanding of what I was actually doing as I eventually just was throwing things at the wall until something stuck. Which is how I finished those exercises on the final lab. Other than that it was all very straightforward and is a great resource to have available.

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. Use linear regression models to fit a straight line or a polynomial curve to a set of data points and predict the output value for a given input value. Use logistic regression models to classify data into two categories and estimate the probability of belonging to each category.

Outstanding beginner level course that introduces regression and classification with Python. The class is light on the math and coding, but it gives a fantastic overview of the topics, and provides excellent visualizations to build intuition. Andrew Ng also provides a lot of very useful tips for machine learning practitioners (i.e., we don't use linear regression for classification problems!). Very much recommend this course for anyone, whether you are a seasoned ML developer, or you want to just start your journey into the field.

I was unsure when I started if this was a good fit for me. It took me abit to wrap my head around the formulas as I am use to seeing them different in my coding and less math looking. but thanks to the charts and examples it made it much easier. I had to show this to a friend and ask him do your algorithms look like this in your code, his reply was only in my nightmares. It opened my eyes though to realize that data scientist are another level or programming all together. recommend even just to understand the concepts.

I've gained so much from this course because it has improved my understanding. The way the course is structured, the hands-on assignments, and the real-world examples have given me a strong foundation in the concepts. Andrew's teaching style is straightforward and engaging. He makes complex mathematics easy to understand. I now feel confident and motivated to delve deeper into the world of machine learning. Kudos to Andrew Ng and the entire team behind this course for creating such a valuable and empowering learning experience!