The logistic regression part really needs rework, It is not so clear for an engineer student and is very confusing at some parts

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.

Programming assignment not giving proper explanation for failure

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:

First and foremost, the course content was exceptional. It was thoughtfully designed, comprehensive, and covered all the essential topics in a well-structured manner. The instructors demonstrated a deep understanding of the subject matter and presented it in a clear, engaging, and accessible manner. The course materials, including video lectures, readings, and assignments, were of the highest quality, providing a rich and immersive learning experience.

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.

Another notable feature of Coursera is its flexibility. The platform allows learners to study at their own pace, fitting education into their busy schedules. The course materials are available 24/7, enabling learners to access them anytime, anywhere. Additionally, Coursera offers a mobile app, making it even more convenient to learn on the go. This flexibility ensures that individuals from diverse backgrounds and geographical locations can benefit from Coursera's top-tier education.

Lastly, the completion certificates awarded by Coursera hold significant value in the professional world. These certificates are recognized and respected by employers worldwide, showcasing one's dedication, knowledge, and skills in a specific subject area. The certificates earned through Coursera courses can greatly enhance one's professional profile and open up new career opportunities.

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!

This course is an exceptional introduction to the world of supervised machine learning, focusing specifically on regression and classification techniques. The instructors are clearly experts in the field, and their passion for the subject matter is evident in every lesson.

One of the things I appreciated most about this course was the level of detail provided in each lesson. The instructors didn't just explain the theory behind each algorithm, but they also provided practical examples and walked through the code step-by-step. This approach made it easy to follow along, even for those who may be new to programming or machine learning.

Another standout feature of this course was the emphasis on real-world applications. The instructors didn't just cover the theory behind each algorithm, but they also showed how they could be applied in a variety of contexts, such as predicting housing prices or classifying images.

Overall, I would highly recommend this course to anyone interested in machine learning. The instructors are engaging, the content is informative and well-organized, and the practical applications are truly inspiring. If you're looking to learn about regression and classification techniques in supervised machine learning, this course is an absolute must!

Supervised Machine Learning: Regression and Classification is a course taught by Andrew Ng on Coursera. The course is part of the Machine Learning Specialization, which also includes courses on Unsupervised Machine Learning and Reinforcement Learning.

The course covers the basics of supervised machine learning, including regression and classification. Students learn about different types of regression models, such as linear regression and logistic regression, and different types of classification models, such as decision trees and support vector machines. They also learn about how to evaluate and improve the performance of machine learning models.

The course is well-organized and easy to follow. The lectures are clear and concise, and the exercises are challenging but not too difficult. Andrew Ng is an excellent instructor, and he does a great job of explaining the concepts in a way that is easy to understand.

Overall, Supervised Machine Learning: Regression and Classification is an excellent course for anyone who wants to learn about the basics of machine learning. The course is well-taught, well-organized, and challenging. I highly recommend it to anyone who is interested in learning more about machine learning.

I recently completed the "Supervised Machine Learning: Regression and Classification" course on Coursera and I must say that I am thoroughly impressed. The course was easy to understand and the concepts were explained in a very clear and concise manner. The instructor did an excellent job breaking down complex topics into simple, digestible parts.

The course was also very insightful. The practical examples and case studies helped me to better understand the theories and how they can be applied in real-life scenarios. The assignments and quizzes were well-designed and provided ample opportunity to practice and reinforce the concepts learned in each module.

One of the best parts of this course was the emphasis on using the techniques in fundamental data science jobs. The instructor showed how the models learned in this course could be applied to real-world data sets, which was incredibly useful. This course has given me a solid foundation in supervised machine learning that I can use in my future data science work.

Overall, I would highly recommend this course to anyone interested in supervised machine learning. It's easy to follow, insightful, and provides practical knowledge that can be applied in the real world.

I thoroughly enjoyed the "Supervised Machine Learning: Regression and Classification" course as part of the Machine Learning Specialization by DeepLearning.AI and Stanford University. This course provided a comprehensive overview of essential concepts and practical techniques in regression and classification. The course content was well-structured, starting from foundational principles to hands-on implementation using NumPy and scikit-learn. The interactive exercises and real-world examples significantly enhanced my understanding of linear and logistic regression, enabling me to build predictive models effectively. Furthermore, the clarity of instruction and depth of explanations by the instructors made complex topics accessible and engaging. I appreciated the emphasis on best practices and the opportunity to apply my learning through assignments and quizzes. Overall, this course not only equipped me with practical skills in supervised learning but also inspired me to explore more advanced topics in machine learning. I highly recommend it to anyone looking to dive deeper into regression, classification, and their applications in data science.

Supervised Machine Learning: Regression and Classification is the very first course of 3 courses of machine learning specialization . This course is very practical in nature . The instructor Andrew NG Sir is GOD . The topics that you will learn are some of the toughest topics in science . But sir has tought these thing in such a way than a beginner will understand easily . I have just completed the course and I am itching to jump to a project and I am confident that I can pull it off without and external support . That's how good Sir has been . Also the way the course has is designed is genius . Labs are the gold of the course . Dont miss it . Also suggest you to make notes by yourself along with watching video . If you are not making comprehensive notes , you might lost track . (If any one need my notes if the course , you can dm me on my twitter or linkdin . Links are below 👇 ) MY TWITTER - https://x.com/adven_raj?t=iwzVPPIZMJTWH-HKm19YSA&s=09 MY LINKDIN - https://www.linkedin.com/in/ashish-raj-230239280?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app

I am very impressed with the quality and comprehensiveness of this course. I can feel the passion that Andrew Ng and his colleagues have for democratising ML, and I love it! I hope that this course goes a long way to making ML more widely accessible, and helps ensure that packages are used with more understanding (and thus producing higher quality analysis). Three aspects in particular that I think helps make ML more accessible are: 1. The pace and the options for additional classes. I'm a mathematician, so perhaps I'm not the best judge - BUT it seems broken down to intuitive examples. And concepts introduced gradually with repetition and well-placed tests. 2. The labs are beautiful! The amount of time that must have gone into some of the interactive graphs is appreciated. 3. The discussion at the end, and the consistent small references to real life applications that are used, expands this work from an academic exercise, which makes it exciting! Huge thanks to everyone involved in this high quality, enjoyable, and motivating course.

I am very impressed by the quality of the content and the instructions. The course covered very important concepts clearly, such as the mathematics and the logic behind machine learning algorithms, such as cost, vectorization, regularization, penalty, and equations. The course also provided enough background and guidance for me to learn more from other sources if needed. The instructor, Andrew Ng, was patient and explained everything slowly and clearly. The labs were very good and sophisticated, and the code in them was useful and helpful. I learned a lot from this course, and I highly recommend it to anyone who wants to learn about machine learning regression and classification. No difficulty was faced to understand everything and the quizzes were relevant and focused on learning instead of grades. The labs were a very important part of learning and helped learn the actual implementation of the concepts. This is the very best course if you are just starting to learn machine learning, the cirriculum and teacher both are perfect.

Excellent way to teach Supervised Machine Learning. One must offer this course if he wants to understand Supervised Machine Learning. I am extremely thankful to the mentor and course designer. If you wish to start learning AI, then this must be your first course and there are more too.

The teaching methodology is excellent. All minor details are very well explained. The code is provided for right methods , also provided for the wrong methods. So, you can compare yourself the correct method.

After completion of this course, I can implement Supervised Machine Learning in my field.

The community, were you can communicate with other helps you to interact with other learners and mentor.

Online mode of this course makes it most favorable, as you can learn with your speed and at your time with minimum cost. The cost is very very less compare to the other courses of this quality.

Thanks again to all teachers and staff. : Sunil H. Ganatra, Nagpur City, Maharashtra, India

The course is very easy to follow, building slowly enough and with enough examples that it's usually simple to understand, and then, looking back, you discover that you have learned something quite complicated. I have enough basic coding experience in python to handle basic functions such as those in this course already, so I found that part quite easy; this may not be the case for those with no python background at all.

Andrew Ng has a great teaching persona, and it's a real pleasure to watch the videos, even aside from what I'm learning, just because the vibe is so cheerful and supportive. As an educator and teacher trainer, I can be quite critical of how courses are taught, but this one is just a joy. I feel like there's a lot for me to learn from Andrew about teaching.

The only (minor) quibble I have is that the final lab is a bigger jump in difficulty than I was expecting, but there is definitely enough help provided within the lab itself that it's still doable.