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!

The Machine Learning with Python course provides a solid foundation in the basics of machine learning, with a focus on practical implementation using Python. It covers key concepts like supervised and unsupervised learning, regression, classification, and clustering, alongside popular libraries such as Scikit-learn, Pandas, and Matplotlib. The course is beginner-friendly, making it accessible for those new to machine learning. However, some may find the theoretical depth limited, so it's best for hands-on learners seeking an applied approach to machine learning rather than a deep dive into the mathematics behind algorithms.

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step. Having worked my way through the IBM Data Science courses, this one was the "pay off" - it was so cool to finally apply more sophisticated techniques to real world data sets. The labs were fantastic. Highly recommend this course to anyone interested in learning about the most popular machine learning algorithms.

This is a Perfect course, except for the name of the course. It is one of the perfect courses for those who wanted to become familiar with different machine learning algorithms (different classification algorithms, as well as different clustering algorithms). In fact, it is the course I definitely recommend for those who want to start machine learning. By the way, I did not understand why the author used this title for this awesome course, given that he is not used Python programming. The best title might be this one, I guess:

"Different machine learning problems, and algorithms "

It's very basic but essential to understand more complex topics regarding machine learning. The Course is well-structured with good presentations. The only issue that I've encountered is with the IBM Watson Studio. For some reason, it just refuses to accept my credit card (which I'm currently using to pay for my other stuff) and boots me off the website every time. I create a GitHub repository for the final project and, fortunately, people have no problem reviewing my notebook on GitHub which allowed me to get around with the issue with IBM Watson Studio.

This course is perfect for beginners. It gives a basic idea about clustering, regression, decision tree, recommender system, classification algorithms along with Labs. You should know a little bit about Python programming and few libraries like NumPy, pandas, sciPy, and sci-kit learn. The Labs are great because you will be using the concepts learnt in the video lectures on the sample datasets and when you see the results, it will motivate you to go for some hands-on projects from Coursera Rhyme Project Network and it will be beneficial for you.

I recently completed the "Machine Learning with Python" online course and I highly recommend it. The course provides a comprehensive introduction to machine learning using Python. The content is well-structured, the explanations are clear, and the practical exercises enhance understanding. The instructor is knowledgeable and engaging. The emphasis on practical implementation and model evaluation is a major plus. The course is user-friendly and allows for flexible learning. Overall, a fantastic course for anyone interested in machine learning!

This course is an excellent platform to understand the basics of Machine Learning with python. The lab tools pioneer a way to understand the code and implement it. The videos are crisp and clearly mention the scope of the course which creates a curiosity to know more. However, the peer graded assignment is not an efficient way as 'sample notebook" paves the way to plagiarism. The peer grading also restricts the user creativity to write a simpler code as it may not be understood by other peers. Overall I am very happy with the course

I absolutely loved the "Machine Learning with Python" course by IBM! The practical labs and concise, explanatory videos were a real highlight, covering a range of supervised and unsupervised learning algorithms, including regression, classification, and clustering. Learning metrics such as accuracy, precision, recall, F1 score, MSE, RMSE, MAE, and log loss, as well as gradient descent and cost function explanations, was particularly helpful. I also appreciate the quizzes, which helped me to check my knowledge.

Highly recommended!

The **"Machine Learning with Python"** course on Coursera offers a hands-on introduction to machine learning concepts using Python. It covers data preprocessing, model training, and evaluation techniques with libraries like **Scikit-learn**. You'll learn about algorithms such as linear regression, decision trees, and support vector machines. The course includes practical projects and quizzes for real-world experience. It's ideal for learners with basic Python knowledge and an interest in data science or AI.

Excellent content and relatable use cases. As a beginner in data science with no formal programming, the information is presented in a way to help you understand the fundamentals and then apply them using the pre-built python packages that are widely available. I started with data science 3 years ago and it was very difficult to get started without any programming or statistics background. This course does a tremendous job of making it accessible, understandable and quite frankly a lot fun in the process.

I wanted to thank you both for the wonderful Machine Learning with Python course you have created. I found the course very informative and thought-provoking. I learned a great deal about applying machine learning techniques to data science projects. I really appreciate the way you presented the course and the real-life examples.

Thank you for giving me the opportunity to expand my data science skills. It was a pleasure taking your course and I look forward to learning more from you both in the future.

This course is fantastic, It has adequate amount of theory supplemented by labs. I also like the Watson Studio, and the fact that you actually learn to use some industry level tools in this course really takes the icing on top. The staff is supportive and wonderful, the community and cohorts are great. Overall I would happily recommend anyone who has absolutely no knowledge about Data Science to start right here with this course. Really enjoyed and thank you IBM for you digital badge. :-)

Well structured course, which will give you understanding of the applied way of working. The topics are explained in quite enought details, allowing you to use learned approach in practical way.

What I would personally wish - a bit more examples of different kinds. It should not be included into main structure of the course (to decrease a work load of Instructors and Students). It needs to go into Optional part, but I'm sure - who is interested in, will finish the task.

This course needs basic knowledge on algorithm and programming experience. I really recommend this machine learning course for those who have computer science, statistics, or math background. The instructor is very clear, concise, and using simple diction when explaining the subject. All presented in here is valuable and worth reading and listening. The final task is somewhat challenging, but we'll have to really dig into the examples presented in the labs. Thank you!