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!

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!

This course was perfect, especially in my situation. I know all of the math behind neural networks, and fitting, but there were many algorithms I've never been exposed to - and this course exposed me to a lot! I liked the hands-on coding labs and learned where to find a lot of Python stuff that I wasn't aware of. A lot of terminology that I'd heard about is now clear in my mind. And the amount math was balanced perfectly with the getting things done.

I really enjoyed taking this course. The instructor is to the point, crystal clear. Nicely explains the essence of the topics in 5 to 6 minutes. I recommend this as a good introduction course to get a basic overview of different algorithms. However, if one wants a deeper understanding with specific details, this is not the course. This course will definitely serve as a good introduction which help us to get motivated to do more advanced courses.

I really enjoyed during this course . Gives you a lot of skills of how to deal with data ,predictions or recommendations. At the end i know how day to day life works based on machine learning as they quite kept few real world examples while explaining. Little bit of difficulty i faced while doing main project as there was less guidance on what we have to show at the end of project. But it was a great course. Worth spending time over it.

This course will challenge learners to commit to learning about the key objectives for using algorithmic approaches to answering important business questions using data. The lectures cover the theoretical foundations of the "relationship" algorithms used for classification and clustering methods. Additionally, the labs provide a fully integrated environment in which learners can do hands-on investigations to gain proficiency.

The course was enlightening. The course is very well designed in terms of ease to follow from one to the next step. Concepts are well described along the way. There is plenty of room to try out different models and learn the next piece of the puzzle. Everything falls in place when you finally reach the capstone exercise. I recommend this course especially to those, like me, who love numbers! I enjoyed the course very much.