If some small project type of stuffs are added in the course, that would be more of a help.

very good course

From the enthusiastic ratings given to this course, I had thought it was really math centered and hard to attend. Honestly the course is all but hard if you have a very basic knowledge of python and calculus. 20 years after having attended my calculus 101 lectures, I'm still able to follow very easily mr Andrew. I hope the course gets harder and more challenging with the sections 2 and 3 or I'm afraid I'll have wasted my money.

iIt was my frist machine learning course , learned many concepts and this course created more interest in learning advanced algorithms and explore much more concepts

It is a good course for complete beginners, but for those who want to know things in detail, this course just doesn't quite cut it. It skips the details too much.

I passed the "Machine Learning" course by Andrew NG in 2019, and I took this course as a part of my specialization. So this course looks like a simplified and cut version of its predecessor. Most of the Lab files are not downloadable as PDFs, so, after the course finishes - you can't have that data with you. Quizzes are so much oversimplified, like 2 questions, I think in the previous course it was much harder and it took me time to learn things. This review is a bit chaotic, I am sorry for that. I know that Andrew Ng is a great teacher, and in the previous course, he gave so much more valuable information and the simplification of that course makes me sad.

I found it hard to follow and confusing. By the time, we finally got to do some hands on work, I felt totally unprepared. Could have done more to explain the python code or to work the student slowly into the coding assignment. All in all, I can not say if it a good specialization, yet after the first course I am looking for something more hands on and beginner friendly. I am a bit disappointed as I have heard only stellar things about this course.

The quizzes and labs are too easy to be of value, with many of the quiz answers literally being written in the image displayed above the question, and labs basically just require you to translate a specific equation into Python.

There isn't really a way to tell if I understand the content or not, I recommend you do not pay for this course.

I cant seem to access my optional lab materials even when i have not exceeded deadlines and dont have have an outstanding payment. it keeps telling me session timed out and keep repeating the same when i click the reopen button. i really enjoyed the course but i didnt like the fact that i couldnt access my optional lab material.

Very, very very basic. If you're looking to freshen up some concepts, this is not for you. I wouldn't even recommend this course to CS students as the course avoids at all costs at explaining the maths behind machine learning.

I was expecting something more challenging and more in depth

Even after paying the full amount for the course upfront, coursera locks optional labs and assignments if the deadline is not met. It asks me to buy the whole specialization series again! My god! Online courses should be flexible w.r.t. timelines. Whenever I find time, I use coursera to learn new things. But this limited time content availability beats the whole purpose. Very very very disappointed.

Absolutely not worthen this money. This course is suitable only for non-technical people without any experience in ML/DS.

there are many mistake i hope you solve it ...it loss my time

no refund on 5.24 when last payment is 5.20

shit-ass course.

unenrolled

I recently had the privilege of enrolling in a course on Coursera, and I must say it was an extraordinary learning experience that I wholeheartedly recommend to anyone seeking quality online education. Coursera offers an extensive range of courses from renowned universities and institutions, ensuring top-notch content and expert guidance. The course I undertook exceeded all my expectations, and here's why I highly appreciate and recommend Coursera:

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One aspect that truly stood out was the interactive nature of the course. Coursera incorporates various interactive elements like quizzes, hands-on exercises, and discussion forums, fostering active participation and reinforcing understanding. The platform also offers opportunities for peer interaction, allowing students to collaborate, share insights, and learn from each other. This collaborative learning environment added a valuable dimension to the course, making it engaging and dynamic.

The support and feedback provided by the instructors and teaching assistants were exceptional. They were highly responsive, providing prompt and insightful responses to queries and concerns. The feedback on assignments and assessments was detailed, constructive, and helped me enhance my learning and skill development. The instructors' commitment to their students' success was evident throughout the course, creating a supportive and motivating learning environment.

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In conclusion, I cannot praise Coursera enough for its outstanding online courses. The quality of content, interactive learning experience, exceptional support, and flexibility provided by Coursera make it a top choice for anyone seeking to expand their knowledge and skills. I wholeheartedly recommend Coursera to all lifelong learners, professionals looking to upskill, and individuals seeking high-quality education. Enroll in a course on Coursera today, and embark on an enriching learning journey that will undoubtedly shape your future success.

I am thrilled to share my experience with the Coursera course "Supervised Machine Learning: Regression and Classification." This course has been an incredibly enlightening journey into the world of machine learning, and I am immensely grateful for the opportunity to learn through this free course. From the very beginning, the course structure was clear and well-organized, making complex topics accessible and manageable. The instructors did an outstanding job explaining key concepts in regression and classification, providing real-world examples that helped solidify my understanding. The combination of theoretical lessons and practical exercises ensured a well-rounded learning experience. One of the highlights for me was the hands-on programming assignments. These exercises were not only engaging but also reinforced the material covered in the lectures. The feedback and peer reviews were invaluable, allowing me to see different approaches to the same problem and learn from my peers. The availability of resources and the interactive nature of the course made learning enjoyable and effective. I appreciated the supplemental readings and videos, which allowed me to delve deeper into topics of interest. The discussion forums were a great platform to ask questions and share knowledge with fellow learners. I am particularly thankful for the free access to this high-quality course. It’s incredible to have the opportunity to learn from world-class instructors without any financial burden. This course has undoubtedly expanded my knowledge and skills in machine learning, and I feel more confident in applying these techniques in real-world scenarios. In conclusion, I highly recommend the "Supervised Machine Learning: Regression and Classification" course on Coursera to anyone interested in machine learning. Whether you are a beginner or looking to enhance your existing skills, this course offers a comprehensive and gratifying learning experience. Thank you, Coursera, for making this exceptional course accessible to everyone!

The course was marked as beginner level, and I think that is a correct characterization. I appreciated some of the deeper dives into the mathematical underpinnings, and felt they struck a good balance between showing some of the underlying math without making it the focus of the course. I think I expected a bit more breadth in the coverage of different types of AI models and techniques - beyond just linear regression and logistic regression, which I wouldn't normally think of as AI models at all. But as someone with a lot of background in statistics but little knowledge of AI, I was interested to see the slightly different AI-flavored spin on these basic model types to discuss topics like gradient descent, feature engineering, regularization, and more that were new to me. I would have appreciated a bit more in the way of Python instruction or guidance about resources for Python help, but they provided a lot of resources that I think will be helpful reference for writing my own code. I might have been interested in one or two (optional) code exercises that would have forced me to walk through an analysis from start to finish as an opportunity to practice the actual implementation of these techniques - e.g. importing data, creating a simple plot, running a regression, using scikit-learn. But I also understand that this would have added to the number of hours required to complete the course, and I was very appreciative that it did not take too much time out of my day/week to complete all the material - as I do have a full-time job! Andrew is a great lecturer, and did a great job explaining concepts clearly and presenting the material in an engaging and interesting way. I think this was the best part of the course.

Dear Technical Team and Professor, I would like to take a moment to express my sincere appreciation and gratitude for the outstanding work done by the technical team in designing the labs and practices for the machine learning course. It is evident that their exceptional teamwork and collaboration have contributed to the success and effectiveness of the course. The labs and practices provided valuable hands-on experience and allowed us to apply the concepts we learned in a practical setting. The level of attention to detail and thought put into designing these exercises was truly commendable. Each activity was structured in a way that fostered learning and allowed us to deepen our understanding of the subject matter. I want to extend a special thank you to every member of the technical team for their dedication, expertise, and effort in creating such engaging and insightful learning experiences. Your commitment to excellence is evident in the quality and effectiveness of the labs and practices. Finally, I would also like to express my gratitude to our esteemed professor, Andrew NG, for his guidance and leadership in implementing these learning materials. His expertise in the field of machine learning clearly shines through in the carefully crafted labs and practices. Once again, thank you to the technical team and Professor Andrew NG for their outstanding work in designing the labs and practices. The impact you have had on my learning journey cannot be overstated, and I am incredibly grateful for the opportunity to have benefited from your expertise. With heartfelt thanks, Saeed Vatandoost

This foundational course, taught by the renowned AI expert Andrew Ng, provides an excellent introduction to key concepts in supervised machine learning. What I Liked: Practical Focus: The course emphasizes hands-on learning. I appreciated the opportunity to build machine learning models in Python using popular libraries such as NumPy and scikit-learn. Linear Regression and Logistic Regression: The course covers both linear regression (for continuous prediction tasks) and logistic regression (for binary classification). These fundamental techniques are essential for any aspiring data scientist. Strong Theoretical Foundation: While practical implementation is emphasized, the course also ensures a solid understanding of the underlying theory. This balance between theory and practice is crucial for effective learning. Key Takeaways: Linear Regression: I gained proficiency in linear regression, understanding how to model relationships between input features and continuous output variables. Logistic Regression: The course demystified logistic regression, which is essential for classification tasks. I learned how to predict binary outcomes effectively. Python Skills: By working with real-world datasets, I improved my Python skills and gained confidence in implementing machine learning algorithms. The “Supervised Machine Learning: Regression and Classification” course lays a strong foundation for anyone venturing into the field of machine learning. Whether you’re a beginner or seeking to reinforce your knowledge, this course is a valuable resource.

Brilliantly Designed course to teach beginer on Machine Learning. The course focuses on the theory behind machine learning. The content convered in the course allows the student to get an intuitive idea behind machine learning and gives him an idea of the mathematics behind it. The course is not very math intensive, but there is just enough math covered here to give the student an intuitive idea of machine learning.

The coding labs provide very detailed code, which the user can learn and analyze to make his own machine learning algorithm

My favorite part about this course was how neatly the jupyter notebooks and python files of the lab were arranged and provided. These lab files take the burden of coding from scratch away from the students, and allow students to focus only on the algorithms behind machine learning.

After this course, machine learning codes will no longer be a black box, but will be something you will understand very well. So, after doing this course, the next time you use Machine learning libraries like SciKitLearn, you will know exactly what is going on behind the curtains, can you can adjust parameters of ready-built ML funcitons to fit your needs.

At the end of this course, you will learn how you can modify machine learning codes for each custom need, and you will gain the ability to do those modifications yourself. After this course, you will be able to write specific machine learning codes which are well suited for a different specific application

This is an awesome course! Professor Andrew does an impressive job in teaching, usually touching the right spots, and addressing in advance potential doubts or complex subject's difficulties, making our learning journey smoother. The Jupyter notebooks are really useful and focus on what really matters, usually giving the impression that no unnecessary details were included, which stimulates us to keep doing it fully. I usually give full attention in watching the videos in the first pass, but, when needed, I make a second one with a faster play speed to support the drafting of my lecture notes, so it would be useful to have some kind of preset "checkpoints" for us to jump directly to the relevant video spots. Also, it would be useful to have a summary at the end of each video containing some kind of bullet points with the most relevant conclusions taken from that lecture to help solidify what was exposed. Lastly, the details of the partial derivatives calculation apparently are not in the scope of this course as clarified by the professor, but it would be nice for us to have a better idea of how the final formula is reached, at least under a high-level perspective, maybe by expanding a little further the optional slides on these sections, one example is that both linear and logistic regression have the "same" gradient formula despite having completely different cost functions.

The "Supervised Machine Learning: Regression and Classification" course by DeepLearning.AI is an outstanding introduction to the world of machine learning. Taught by the renowned Andrew Ng and his dedicated team, the course offers a well-structured and comprehensive overview of key concepts in supervised learning, specifically focusing on regression and classification techniques. One of the highlights of this course is its clear and engaging presentation of complex topics. The lessons are thoughtfully designed, with a great balance between theory and practical application. The inclusion of real-world examples and hands-on exercises helps solidify the learning and provides a deeper understanding of how these algorithms work in practice. The course also excels in its accessibility; the explanations are easy to follow, making it suitable for both beginners and those looking to refresh their knowledge. The step-by-step guidance through coding exercises in Python is particularly beneficial, as it allows students to apply what they've learned directly in a practical context. Overall, this course is a must for anyone looking to get started with machine learning or enhance their skills in regression and classification. The dedication and expertise of the instructors make it a truly enriching learning experience. Highly recommended!