Course is largely a scam. At the end you have to have a peer reviewed project that will prevent you from finishing the course if other people do not grade your project. You can have a high enough overall grade that you could get a 0 on the final and still pass and still be out of luck if people decide to not help with grading, which is exactly what happened to me. Do not waste your time and money if you want to be at the mercy of other people.

"An Exceptional Introduction to Machine Learning with Python" This course is a masterfully designed gateway into the world of machine learning. It balances theory and practical application beautifully, ensuring that concepts like supervised and unsupervised learning are not just understood but applied effectively. The use of Python libraries such as Scikit-learn, Pandas, and Matplotlib is seamlessly integrated, allowing learners to develop both programming skills and machine learning expertise. The step-by-step guidance in building models like Linear Regression, Decision Trees, and SVM, paired with hands-on assignments, makes the learning process engaging and rewarding. The instructors explain complex topics with clarity, making this course accessible even to those new to the field. I truly appreciate how the course encourages critical thinking by having us evaluate models, optimize performance, and interpret results. By the end, I felt confident applying machine learning techniques to real-world datasets. Overall, this course is a must for anyone looking to build a strong foundation in machine learning with Python. It has exceeded my expectations and is well-deserving of 5 stars!

1. Improve their work: Constructive feedback from peers or instructors guides learners to refine their skills and understanding. 2. Learn from others: By reviewing peers' work, learners can gain new insights, perspectives, and approaches to problem-solving. 3. Develop critical thinking: Evaluating others' work enhances critical thinking skills, as learners analyze and provide feedback. 4. Earn grades: In some courses, peer reviews contribute to the learner's overall grade. 5. Enhance learning experience: Reviews foster engagement, community building 1. Peer review: Learners evaluate and provide feedback on each other's work. 2. Instructor review: Instructors provide feedback and guidance on assignments. 3. Self-review: Learners reflect on their own work, identifying strengths and areas for improvement

Lots to learn in this class! Week 3 was definitely heavy and challenging in the middle of it, but the course really builds up well and makes sense by the end of it and I understand why those topics were combined as they were. I found the labs most helpful when they included # hashtag explanations/documentations when introducing new code to explain the different parameters and reasons for using them, or if establishing parameters in the code with explanatory definitions/names to guide the user through new operations. In the very last lab, I think they included a link to the pandas API reference page with that specific new operation. I found that really helpful because I had already been going to the pandas page to learn more about other new operations as they were introduced in previous labs.

I just finished the Machine Learning course, and it was fantastic! The curriculum was well-structured, covering both basic and advanced topics in an engaging way. The instructors were knowledgeable and provided real-world examples that made complex concepts accessible. The hands-on projects were invaluable, allowing me to apply what I learned. Plus, the supportive community made discussing challenges and sharing insights easier. If you want to dive into machine learning, I highly recommend this course. It’s a great opportunity for anyone aiming to enhance their skills and advance their career!

Machine Learning with Python is highly informative and very well presented. It wasn't easy, it requires a good understanding of math. Complex concepts of machine learning algorithms are explained clearly.

After the course, you will have a solid awareness of how machine learning is applied to the real world and how to use the skills like, sci-kit learn and SciPy from the Python language.

Excellent support of the labs and the Notebooks provided. The final project will be a challenge for what we have learned.

I strongly recommend this course.

This was MILES ahead of the last IBM course I took (Building AI Application with Watson APIs). The part that I thought isn't great is the use of other students to "grade" the final project. I definitely understand that you wasn't have hundreds taking the courses at any one time, so that might be the best way to get through the projects. I hope that there is some sort of feedback loop so that if a project was failed by a classmate more than twice, the next submission goes to a REAL staff member for review. Thanks for the great course.

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.

Overall the course was very good and I love the peer-graded assignment concept. As after completing your assignment you can see other's assignments, there you can point out where you are better than others and where you lack.

One thing to be noted is that the algorithm training part totally in the practice session. So you have to first read/understand the code by yourself then you can implement it. I think the course could be better if video lectures where there for algorithm training part.

I recently completed the 6-week machine learning course and I can confidently say it was an excellent experience. The course provided a solid foundation in Python and machine learning, covering key topics like data preparation, model evaluation, and core algorithms such as linear regression and decision trees. I especially appreciated learning how to optimize model performance through techniques like hyperparameter tuning and cross-validation.

Highly useful ML course for students and professionals. Modules are of top quality. Good explanation of tricky topics like SVM, KNN and classification. Thank you Coursera and IBM for bringing such as high quality and affordable course.

Excellent contents and Hands on Practice at IBM Watson Machine Learning is Great Experience.

Thank you for this wonderful course. Like to learn more form such course.

we learned a lot beyond this course.It really explained the Machine learning from basic to the intermediate level and also huge coverage of techniques in python

The course is amazing but the labs are on an external website that is so bad (Sometimes it does not load, ).

Amazing teaching By IBM , each and every lab were sophisticated and made an overall good Outcome.

Highly recommended! Good intro and explanation of details of each model and great hand on labs

The explanation and the material provided are good and precise with relevant exmaples.

Excellent content, explained and delivered well.

Pretty Good Course, to start is perfect

one of the best course for ML

The course is outdated in some aspects. I wish it used more real world problems, and there was a deeper explanation of how the data is treated. The notebooks are used in a third party, which makes the learning experience worse, since the third party system is not automatically graded. For an introduction is ok, but it definitely needs an update on the whole course.

Concise presentation,brief and to-the -point explanations, great course for an intermediate ML developer looking to brush up their skills.Programming exercises should me more detailed.

I liked the concept of peer graded final project allowing us to review the projects of other learners as well.

I enjoyed this course and thought it was a good high level overview of machine learning. I appreciated the exposure to Jupyter notebooks, but the coursework could have been more python programming focused. There was not much learning of the python language in the course.

somewhat superficial. I think the course name should be only machine learning, not machine learning with python. There is no tutorial with python.

Nice Course, but I think it should go a little deeper in the math fundamentals of machine learning and explore more algotithms