"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!

Very well structured course with great explanations in the appropriate pace. The maths are discribed clearly and the connection between algebra and algorithms (Machine Learning) becomes and easy process.

The assignments are in the indermediate level and the student should understand the theory/maths to complete them with 100% grade. They are all explained in the lectures videos but you need to think before you submit them.

Overall, is an upgrade of the previous course that is adjusted on Python and Jupyter Notebooks. 5/5 stars.

Fantastic learning experience. A novice with little or no technical knowledge can grasp the essence of Supervised Learning pretty rapidly. Yes, the coding aspect requires a bit of focus & practice no doubt. Just this course alone, will expand your ML knowledge & confidence to solid levels. You begin to get a good feel for the jargon of AI/ML. Highly recommended for newbies, execs and folks looking to make that career shift in a systematic way. Dr Andrew Ng is pure genius with simplicity at his core. Thank you Dr Andrew Ng.

This course held significant value for me. While I typically appreciate all course content, what truly stood out was the exceptional communication skills of the teacher. Having been self-taught for a major part of my life and engaging in online courses, I've experienced a tendency for monotony that eventually diminishes my enthusiasm. As someone who also enjoys teaching, Andrew Ng's instructional approach reignited my excitement and instilled hope that teaching in such a dynamic manner can indeed be highly beneficial.

The course is very well structured and presented and the concepts are so well explained that it is easy to grasp them and I had a lot of fun time getting a more in depth knowledge about ML. I am a Product Manager and was looking for a course which gives me a good overview about the mechanics behind ML but also for which problems it is suited and since I could not find a course for just PMs I decided to take this one and didn't regret my decision for a second. Every PM working on a ML projects should take this course.

Very well put together materials and instruction. I had taken Ng's earlier course on this topic ( using MATLAB/Octave ) about five years ago and I found much of the details to be rather opaque due to the highly vectorized ( albeit elegant ) code. Breaking all the components open explicitly using loops in Python gave me a more effective framework for learning in this version of the course. Ng and his crew have nailed it here - a 'goldilocks' treatment of the material - not too difficult and not too easy either.

First of all thank you very much Coursera and team and very big thanks to prof. Andrew for give the opportunity. I am truly Satisfy with this course and throughout this course i learn so many things for the basic which new for me at least.. i will use my this new knowledge for better future for me along with society. Once again thank you very much team Coursera and special thanks to Prof. Andrew Ng. I will also recommend my friend my college who want to learn more and more deeply.

Thanks &Regards.

-Harshil Vasani

While I expected this to be simple Python refresher on the originally taken old course with MatLab/Ocatve, carefully reading into the code before executing it helped to conceptualie what I amd doing more. Also I really appreciate the interative demos, and especially those of gradient descent - they really add so much more to building your intuition -- make sure to click in the horizontal direction more anf more to the right and think why the results are changing in the way they do...

Thanks for this experience!

Thank you for the opportunity to take this course through financial aid! I enjoyed the way the course is structured including the optional practice labs and the programming examinations! Being a graduate student with lots of responsibilities, the flexibility of the course allowed me complete the course at my pace without which I would not have been able to complete the course. I would like to take the remaining 2 more courses in the series to give me a proper grounding in the Machine/Deep learning. Thank you!

I, a 16 year old high-school student, am oddly interested by Machine Learning, never having learnt it in school prior to this. Even though, I lack quite a few skills and had to try hard to understand certain stuff being discussed , the optional labs throughout this course made it easier for me to understand what I was supposed to do and made everything easy. I particularly loved learning and the questions at the end of the videos made me stay motivated to be attentive. Thanks for the wonderful teachings.

It's a great start towards Machine Learning Career, with this course You'll be able to apply supervised machine learning to solve real-world problems with the industry standard and best practices. You will know the application of supervised learning and how to implement supervised learning techniques to get things done without too much efforts and this course provide you hands-on experience and visual learning to describe the concepts and logics that are required in problem-solving and domain expertise

This course was broken down to smaller understandable pieces beautifully. The in-video questions were great and the all the labs were very well designed to soak in all the theory components.

I come from a place where I am scared of Math. The way it is taught helped me embrace ALL the Math seamlessly!

Congratulations to the team!

From a place where I was wondering am I good enough for this learning, to completing the course, congratulations to me as well :)

Can't wait to deep dive into the next course :)

Really good. All the provided codes are amazing, and I found them challenging. The course offers a perfect insight into what machine learning is. Everything progresses as outlined in the program. The videos are not too long, which prevents the audience from becoming tired. The labs are great. The codes for interactive plotting are my favorites, and I spent a lot of time learning and viewing them. I'm happy to have taken this course and recommend it to everyone seeking knowledge in this subject.

A very in depth course that lets you dive in into the world of Machine Learning. I think people that already have some python knowledge will enjoy this course more. Optional labs also aren't really optional, unless you simply want to hear lectures on linear algebra and calculus. In parallel with this course I recommend studying and practicing on modules like pandas, numpy, and matplotlib - that let me enjoy way more the course itself, than what I assume it would have been without some knowledge.

This is an excellent course for beginners in machine learning. Andrew Ng, a world-renowned expert in the field, teaches it. Andrew does a great job of explaining the concepts in a clear and concise way. He also provides plenty of examples and exercises to help you solidify your understanding of the material.

I highly recommend this course to anyone who is interested in learning about machine learning. It is an excellent introduction to the field and provides a solid foundation for further study.

I really adore listening to Mr. Andrew Ng, especially when he tells something along this line, "You don't need to worry about that." This course is very beneficial for me, because I am training to become a Machine Learning Practitioner. What I learned from this course will really be what my job will be. Thank you Mr. Andrew and to the whole team who developed this course. You have developed in me the intuition I need to be an equipped and responsible Machine Learning Practitioner in the future.