If you a newbie in the field of Machine Learning and would like to find the bible of Machine Learning with being detailly instructed, then this course/specialization is absolutely made for you. I love the philosophy of teaching from thay Andrew Ng in a way that he always take all the technical concepts & notations and explains them in math-neutral manner as much independent from math as possible, unlike many other courses which heavily have math terms required for understanding the content. In addition, he guides us always with a question first in mind that is this concept/formula crucial for this purpose, if not, then we skip for now (the master of abstracting the nitty-gritty) enabling me generalizing the whole picture while maintaining a practical orientation approach in both optional and graded lab assignments. A grand appreciation for his great contribution on instructing those content more approachable to the wider set of learners of diverse backgrounds.

Just completed this fantastic course. Learning from Andrew is the best. He authentically cares about your learning and takes you through incremental baby steps to build your knowledge. Don't be intimidated - just start it and you will be hooked! In 3 weeks you can get a really great foundation on how supervised ML works with both mathematical- and python-based formulas/implementations.

The lectures only require a minimal math background - about what you would learn as a college freshman. I used Khan Academy in parallel when I needed a boost. Also - big help - you implement each formula / algorithm in Python code in a series of short, well-focused labs (with lots of pre-defined code). This re-expresses the math into Python code which helps get a concrete understanding of the logic (esp. if you're not a "math person")

I loved this course and sincerely appreciate all of the work from Andrew and the folks who put together the labs to make it a great experience

I recently had the opportunity to take Andrew Ng's Machine Learning course, and I must say, it exceeded all my expectations! The course is masterfully taught by Andrew Ng, a leading expert in the field, who has an incredible gift for breaking down complex concepts and making them accessible to students of all backgrounds. What I loved most about this course was the perfect balance between theory and practice. The assignments and projects allowed me to apply the concepts I learned and truly understand the power of machine learning. I was constantly motivated to continue learning and exploring the fascinating world of AI. Taking Andrew Ng's Machine Learning course was an incredibly enriching experience that has given me the confidence and skills to pursue further studies in this field. I would highly recommend it to anyone looking to gain a solid foundation in machine learning and be inspired by one of the foremost experts in the field

This is an excellent course on supervised lachine learning. The programming assignments are in python.

I have completed the previous machine learning course (programming in Octave ) by Andrew Ng hence I was comfortable with the concepts.

I was new to python and Jupeter notebook. Python implementation part (programming and explanation) is very friendly. I sincerely thank the mentor for immediate help on my problems in programming.

I comleted all assignments succesfully. But the strength of this course is also in the programming material given.This material is comprehensive, very rich and extremely useful. I need to go through in detail. I feel going through course material will help me to be comfortable in reading, writing, developing python programs for ML applications.

A big thanks to Professor Andrew Ng, Mentors and the deep learning community.

I strongly recommend the course for everyone interested in AI/ML.

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.

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 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.

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.

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.

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!

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.

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.

The course helped me to explore the beauty of Machine Learning and has definetly laid the foundations of Machine Learning for the further courses in the specialisation. I would also like to thank humbly and from bottom of my heart to the proffesor Mr. Andrew Ng who made me fall in love with the fundamental building blocks in Machine Learning. The train started from simple Linear Regression which stood so fundamental throughout the course, and gradually by the end , I completed the course without even realising it! There is so much to ;earn and the most fun part of the course were the Optional Labs, where initially I had a hard time, but they proved to be the stepping stones in the course.

I entered in the specialization and I just completed the first course and it was amazing!! The concepts really flowed well together and it gave me a solid pattern of application. This was math heavy but you don't need any math pre-requisite to do any of it. The math is all explained and it is all derived for you with explanations for each step. There was coding in python but the coding examples were problems to show you how to code the actual algorithms and not anything else. Like this course says it focuses on Regression and Classification and the coding examples reflected that. Overall I enjoyed the material I learned from this course and will be doing the rest of this specialization.

I'd tried the original version of this course twice and never completed it due to other commitments cropping up. This time around they've upgraded to Python and gone lighter on matrix algebra, although there is still a section on vectorisation for those interested. Most of this first specialisation was revision for me so I sailed throuh it in a week. I found the jupyter notebooks a bit noisy, being a software engineer and not a data scientist, and tended to delete the skelton code implementations and replace them with the vectorised versions as I actually found this easier. All in all, the video quality has been upgraded and the explanations by Andrew Ng are still clear and insightful.

This was by far the best course for learning supervised machine learning using python as a tool. The optional labs and assignments were to the point while simultaneously taking care to enable students learn the subject with proper hints on various exercises periodically. The visualisation technique for various aspects like gradient descent, sigmoid function etc...via the means of coding ensured that students understand what they are actually doing. Thanks to Andrew Ng sir for personally taking efforts to educate the students.

I am eagerly looking for continuation of this course further on advance machine algorithms which would boost my confidence in carrying out my research work.

This course is excellent! Andrew Ng's enthusiasm for the subject is infectious. Labs are very instructive as they are well-documented and connected with the lectures. Advanced math isn't required but helpful. If you have deeper math background (calculus, linear algebra there are sections of the course where the math behind the lessons are explained further. Andrew focuses a lot on teaching intuition, which is a great way to deepen one's understanding of the material. The interactive graphs are very helpful in this regard. One nit: the Jupyter notebook sections after code blocks get corrupted when errors are made in the code blocks. This was a bit annoying but not a blocker.