I learned a lot, but the final assignment is just a mess - on so many levels. My biggest takeway is that even well-rated courses with qualified instructors end up causing material issues. The quality of the final project implies that people will be trained here to create ML models that will have real world consequences and will not be properly understood or validated. And that's without looking at the huge number of typos, bad programming techniques, and more. Had to give up on the final project due to those difficulties after spending 10 hours of my very early mornings without any reasonable progress.

As am going along in this IBM certification, the quality of courses is getting depleted. This course has by far the worst standard in terms of quality of content and assignments. The worst part is that they encourage you to use IBM cloud services which are the worst and require improvement themselves. But the worst part was the peer guided assignment. With no clear instructions, peers that have no idea checking your assignments and long delay for waiting the grade for it, god help you! Don't waste money on this course. Hopefully, coursera takes actions against IBM if they don't update this course.

I hated that all the instruction was all math and none of it python. The instructor was completely uninspiring. You really should have instructors with some level of charisma. The course was littered with technical errors. This is the worst MOOC I've ever taken. It is a crime that people actually have to pay for this.

Peer reviews are very inconsistent. Submitted a project 4 times following some minor change from one to the other... only to be 2 points from passing. They want you to have an IBM cloud account and push watson services for this only to have the code crash while compiling. No way to reach instructors.

What a waste of time.

Whoever is from the CIS countries beware of IBM's faulty practices.

It contains tons of marketing for IBM products. Without those products you won't finish many assignments. Moreover, IBM blocked my country from the ability to create an account on their services. So, I can not even finish those courses.

In lab there are many funtion , libiraries Which have been used first time with out any description , then I have to search for each and every funtion or lib which is way time consuming which make this course worst courses in my list.

Instructor are going to fast. They are literally reading the slides without proper implementation of the ideas and algorithm explained. Even I can do that, absolute waste of money

wrong grading on quizes (multiple choice questions which are graded 0 or 1), not clear instructions, who write this course? One of the worst courses i took in years

The course was going well with the videos and labs, until the capstone peer-reviewed area. Disastrous instructions, poor supervision and assistance. I am appalled.

Insufficient explanation, bad lab experience, and the final assignment was a nightmare.

Video is short, so you got to figure out things by yourself.

Videos and assignments are very repetitive. It induces students to copy dull code rather than think about solutions

Everything taught int his course ends with a line "unfortunately it is out of the scope of this course"

outdated notebooks, had to google everything anyway

we are not able to get ceritficate

Great course! Get ready to learn, code, debug, sweat, learn some more, fix your code, then finally smile when your ML models work smoothly.

That last statement described my workflow during the final assignment/project of this course.

Quite simply, this course was brilliant because not only did it bring everything we've learned so far together but it also built upon the last course and properly introduced us to Machine Learning and its applications. In his videos, Saeed successfully breaks down complex topics into digestible byte-sized content and ensures that you intuitively understand what is going on.

One of the best pieces of advice I have received in regards to my learning and in life in general is to make sure you have a strong grasp of the fundamentals and these become building blocks to much more complex topics. That in a nutshell is what I believe this course has done for me.

To those who are reading this review, trying to decide whether or not to take this course... just do it! What are you waiting for? No seriously? This might be one of the best decisions you make this year.

If you've been racing through the other courses up to this point, I advise you to slow down once you get here and really try to digest what Saeed has taught here.

Watch the videos, pause, take notes, rewind, continue watching, learn, code. Iterate.

Dear Coursera and IBM,

I am writing to express my deep gratitude for the opportunity to learn new skills and knowledge through your world-class platform. As someone from a rural area in Pakistan, access to quality education can be limited, and I feel truly blessed to have been given the chance to learn on such a reputable platform.

The courses and instructors provided by Coursera and IBM Skills Network have been nothing short of exceptional. I am especially grateful for the professionalism and well-formatted courses, which have allowed me to easily navigate and learn at my own pace.

I believe that the skills and knowledge I have gained through Coursera and IBM Skills Network will be invaluable in my personal and professional life, and I am excited to apply them to make a positive impact in my community.

Once again, I cannot express my appreciation enough for the opportunity to learn through your platform. Thank you for everything that you do.

I love every bit of this course. It is very informative and the explanation by the instructor is second to none. He explained most of the concepts especially using real life scenarios like customer segmentation, detection of cancer and many more. Using these real life examples in the explanation made me understand the course very well and also appreciate machine learning. It will be very easy with anyone with mathematical background though people that are not mathematical inclined may have some difficulties understanding some of the concepts. Nevertheless, going through the lab section will make you understand the concepts very well even if you didn't get all the theoretical concepts. The final project was also centered based on what was taught and easy to follow by anyone that paid apt attention to the lectures and followed duly in the lab exercises. Kudos to the instructor.

The course start with introduction to Machine Learning, with various industrial examples and applications along with libraries used for Machine Learning. Understood how supervised machine learning is different from unsupervised machine learning. Then learnt the concept of Linear, Non-linear, Simple and Multiple regression, and their applications, also how to evaluate your regression model, and calculate its accuracy.  

Practiced with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Introduced with main idea behind recommendation engines, then understood two main types of recommendation engines, namely, content-based and collaborative filtering. The course ends with Peer Graded Assignment to apply all the ML modeling learned.

Thanks to IBM and Coursera for this great learning experience.

I recently enrolled in the Machine Learning with Python course as part of the IBM Data Science Professional Certificate on Coursera, and I couldn’t be more pleased with the experience! The course content is well-structured, providing a clear understanding of machine learning fundamentals along with hands-on practice. The instructors explain complex topics in a simplified way, making it accessible for learners at all levels. I especially appreciate the real-world examples and the use of Python libraries like scikit-learn, which solidified my understanding of various algorithms. The course strikes a great balance between theory and application, allowing me to build and evaluate machine learning models confidently. Whether you're new to machine learning or looking to deepen your knowledge, this course is a fantastic choice!

I am thrilled to offer my comprehensive feedback following the successful completion of the esteemed "Machine Learning with Python" course, thoughtfully curated and presented by IBM on the esteemed platform, Coursera. Throughout this journey, I have been immersed in an exceptional learning experience, artfully blending profound theoretical knowledge with invaluable practical exposure through hands-on exercises. The instructor's profound expertise and lucid explanations have been instrumental in elevating my grasp of intricate Machine Learning concepts and their pragmatic implementation using Python. The interactive and immersive course structure, augmented by compelling real-world projects, has served to fortify my skill set, paving the way for tackling future challenges in this dynamic field.

I personally felt, it is one of the best modules offered as part of certification program. Data science has large number of algorithms, so naturally it is difficult to cover most of them and more importantly it is difficult to decide where to start from. Module is well designed, and it has provided basic to intermediate knowledge of most of machine learning algorithms, must to know for beginners. Few minutes introductory video on any given algorithm, followed an hour-long lab practice is really helped to understand algorithm and it’s implementation using python. Provided structured course really helped me to perform machine learning implementation using python. Great content to spent time on!

Machine Learning with Python is a comprehensive guided path into the insights of Machine Learning. Machine Learning, a term of relatively recent appearance, it is actually utilizing mathematical analysis that dates even centuries ago. Linear regression, logistic regression, are based on mathematical theories with a long history. This particular course, introduce the subject in a clear and comprehensive way suited for all audience with little background on this mathematical theorems. And for those who had the knowledge from previous academic courses of practical experience, the course in very helpful as it allows to refresh the concept in a dynamic and easy to follow pace, in my opinion.

The best way to succeed in this course is to when doing the labs, write down with "hand" every line of code on a separate place, though, you will not understand most of it, just keep going. And then type it on Jupyter notebook from "hand written notes". This process might seem hard effort or seems like no learning is there but trust me this process will get you break the thick wall of Machine Learning and python code. The rest will follow. After following the process, I feel very familiar with code, machine learning algorithms and terminologies which I guess is big achievement. I also believe ISLR can help later in understanding these algos and set up more solid foundation.

Certainly a great course, clear voice and visuals in which the concepts have been explained clearly with rich details. I have noticed many are complaining about the math, lab, coding and the conceptual explanations; so here is a reminder than the course strongly suggested a 'background in Python programing language' in the beginning. Additionally, this is an 'intermediate/ advanced' course for engineers and data scientists, so a well-established knowledge in math should've been already acquired by default, even though the math needed here is very basic and can be done automatically. Also, understanding the conceptual part is very important to perform tasks correctly.

Taking the machine learning course was an incredibly inspiring and educational experience for me! The training material was structured and easy to digest, allowing me to deepen my knowledge in this fascinating area. I especially appreciated the hands-on assignments, which helped me put what I learned into practice and confidently tackle real-world machine learning challenges. The course also provided excellent resources for self-study and further development. Through this course, I gained a valuable set of skills and confidence in my machine learning abilities. Many thanks to the course team for their thorough preparation and professionalism!