I recently completed Coursera's Supervised Machine Learning: Regression and Classification course, and it was an exceptional learning experience. The course provides a solid foundation in machine learning, especially for beginners and intermediate learners who want to delve into regression and classification techniques. The course is well-structured, with engaging video lectures and interactive exercises that make complex concepts accessible and easy to grasp. The instructor breaks down each topic into manageable segments, covering everything from linear regression to decision trees, model evaluation, and even cross-validation. What I particularly enjoyed were the hands-on programming assignments in Python, which allowed me to apply theory to real-world datasets. Additionally, the course offers plenty of resources for further exploration and practice, including quizzes and graded assignments that reinforce key concepts. By the end of the course, I felt confident in my ability to use various supervised learning techniques for regression and classification tasks and evaluate model performance accurately. Overall, I highly recommend this course to anyone interested in building a strong machine learning foundation. It's a practical, insightful, and enjoyable learning journey.

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.

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.

"Supervised Machine Learning: Regression and Classification" is an outstanding course that provides a solid foundation in machine learning concepts. The course structure is well-organized, with clear explanations and practical examples that help bridge the gap between theory and application. The hands-on coding exercises and real-world datasets make the learning experience engaging and impactful. The instructors do an excellent job of breaking down complex topics into digestible segments, making it accessible for both beginners and those looking to reinforce their knowledge. The emphasis on both regression and classification techniques ensures a comprehensive understanding of supervised learning. Overall, this course is a must for anyone looking to delve into machine learning and build a strong skill set in data science. Highly recommended! 🌟🌟🌟🌟🌟

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).