Opiniones y comentarios de aprendices correspondientes a Applied Machine Learning in Python por parte de Universidad de Míchigan

Really really good introduction to applied machine learning. It resolves the fear from the difficult application of complex mathematical formulas. It demystifies the topic of machine learning and provides a perfect introduction how to approach real world problems.

One of the best courses I have ever taken. I wish I would have taken this course earlier. it gives provides you with a lot of practical tools in a shortest time. This course is perfectly designed and the instructor conveys information in the most efficient way.

Following the first 2 sessions of this specialisation, this one seems easy and gives the student a lot of confidence. Make sure you follow the sequence suggested in this specialization, even if you do not plan to continue with Text Mining and Social Networks.

Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world. Many thanks to the organisers and Coursera.

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

Excellent introduction into machine learning with Python. I came into this class with little knowledge of machine learning and was taking this to aid in my data science career. As a result of this course, I've decided to focus more on machine learning.

This course does a really good job taking you through the basics of ML through use of Scikit Learn models. It goes over a broad swath of models in a black-box fashion so you can start getting a feel for how each model is tuned and what parameters to use.

Assignments and quizzes help you a lot in consolidating the concepts. However, some questions in quizzes are tricky but not in a way that really adds to your understanding of the topic. Overall a pretty good course. (4.5/5 is the rating I would give)

This course is well-structured and I learned a lot from it. Students who use retrieval practice, which is a form of self-testing, retain the information longer and learn better. I liked the quizzes and the assignments, and I wished there were more.

The course content is excellent and the instructor makes stuffs easier. Few assignments are very tough but if you go through the course properly can able to solve it. One request to the instructors to a bit slow the pace for better understanding. :)

Though it just give us a limited amount of information about Machine Learning, it really drive me into the novel world of this field.The course told me a lot of basic concepts about ML, thus I can go through many thesis related to the realm, thanks.

Very practical lectures for implementing machine learning. Provides more hands on experience and you can get familiar with python machine learning libraries with this course. Highly recommend if you want to really practice machine learning coding.

The course ire great and illustrates many useful topics. The only thing it needs to improve is about the assignment 4 which requires more information to solve the problem, in particular, people who deal with the complete machine learning problem.

The programming assignments where though because the automatic grader was very picky. Please change it so it gives the user more input about what part of their code is wrong. Also Have a repository where the user can retrieve previous submissions.

Excellent course !!!!! very useful for people who have just completed python and wanted to apply the language. Much more clear when we do the course after studying the libraries of python , very clear explanation throughout the entire course .

It was a great learning experience. The way the course structure is curated is truly adapting to the current trends in field of ML and AI. Thank you for giving me an opportunity to learn from best teachers on a great online learning platform.

Fantastic course theory and material. Additional vague pointers would have been useful for Assignment 4 to help understand required data manipulation not included in the notebooks.

Many thanks to the team and Professor Kevyn Collins-Thompson

A ton of learning, a challenging & rewarding course, the final assignment incorporated concepts & techniques from the first and second courses and gave me a clearer understanding of choosing and implementing machine learning algorithms. :-)

The course had the right amout of labs and lectures to experiement the different algorithms and their theory. The auto grader took a while to understand but the the discussion forum threads were immensely helpful, particularly of Sophie's.

This was a great course, with broad coverage of the topic and practical application in Python with scikit-learn. Challenging quizzes were part of the learning context. Overall a great experience, and the best course in the specialization.

Excellent overview of many ML algorithms. Challenging quizzes and assignments. The only downside is that some functions like fit_transform, decision_function, predict_proba could have been explained a little better. Great coverage though.

Awesome.

I learned a lot of fundamentals machine learning. The lectures are very clear and the assignaments focus on practical examples.

I recomend this course for everyone who want to have a global view of machine learning.

I enjoyed a lot.

I thought this was a fascinating course that tried to do the near impossible and succinctly summarise the key techniques of machine learning. And it did that very well. Very challenging tasks, but also overall inspiring for the next step.

This was an amazing course that I absolutely loved working on. It gave a deep insight into machine learning. I gained a lot of knowledge from this course. A must for the students who are just stepping in the field of Machine Learning.

It's a nice course, that accomplishes what it promised: overviewing ML algorithms from an applied perspective; however, I think that some other model selection methods (especially when comparing regressions) should have been included