This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!
By Gu X•
Most of the content professor taught are intuitive, but the PPT seems helpless. Furthermore, the thinks in the course are shallow depth, conversely the assignment are little bit difficult especially on assignment4. I mean if the goal is to train our to do some real world data you may can shrink the dataset, the large dataset would takes more time to training which would cost more time to debug. Anyway, this is a great course but I think it's better to do slight change on the quiz and assignment.
By Pablo S•
This is a good introduction to applied machine learning with python. Although it is "applied" it would be worth to cover the basics of the presented algorithms a bit more thoroughly. In paticular, I think that the regularizations parameters and their role in bias and variance are not presented in a very clear way. On a different topic I think that the course deserves to be updated with latest sklearn implementations and correct a lot of bugs in the assignements and lectures.
By Melanie B•
This course helped me to get started on using Python for machine learning tasks.
Personally I would have preferred a more mathematical approach when discussing the various machine learning techniques, in order to learn more about what's going on "under the hood" in scikit-learn. I know that the course is called "Applied Machine Learning in Python", but to me it felt more like "Extremely Applied Machine Learning in Python" :-) Other than that, I enjoyed this course!
By Carl W S•
There is a lot of good material in this course, but it is noticeably not taught as well as the previous two courses in this specialization. The lesson plan feels like a class lecture modified just barely enough to work as a MOOC, the autograders are highly finicky, and most of the programming assignments had errors or missing details that required the learner to check the class forums to find out how to fix them. Overall, it was a helpful course, but felt unpolished.
By Daniel K•
I do think the lectures are very well done and I believe I learned a lot. However the programming assignments part was frustrating, there are a lot of issues with the autograder, loading files etc. I would appreciate if the steps were described in greater detail. Some parts are very easy, just blending together a few pieces of code from the lecture, and others very difficult, built on things not covered in the lectures. The last assignment was the perfect balance.
By Thiti C•
This course is, in fact, excellent. One can learn a number of algorithms used in a machine learning practically. This course does not focus much on mathematics behind tools we used, the professor taught a lot about the practical one. However, some of the parst in this course are too rush; you have to understand a lot of concepts in Python berfore entering this course, including basic Python syntaxes, and practical libraries such as Numpy and Pandas.
By Adithyan U•
The course tries to do too much in four weeks. Consequently, the teaching material isn't as comprehensive as it ought to be. I've probably spent over 10-15 hours cumulatively on other websites, trying to comprehend the intuition behind the algorithms used. This course isn't great at getting that across. There's a lot in here that we're forced to take for granted. I'm afraid I'll have to think twice before I choose other UMich courses in the future.
By Charles L•
The material seemed ok. Really annoying that this course genuinely had incorrect code in the homework assignments. It seems that some documents changed directory and were different in the homework folders, vs the grading tool. resulting in failed grades where tests worked just fine. Easily fixed, but why would I have to? Really hurts the notoriety and reputation of this program to have such simple frustrating errors. (on 3 of 4 assignments!)
By Amit S•
It would be better if this course was not with Jupyter notebooks. Professional data science projects will not use notebooks but script files instead. The course should prepare students for professional projects by using script files.
Also the lecturing is very rigid and scripted which makes it less engaging. There is also no material on how any of the algorithms work in detail however there is good material on scikit-learn.
By Koo H S•
While the course material is very helpful and reasonably pace, I felt like I'm always battling the autograder to pass the assignment. I do think that I spend more time to get my answer accepted by the autograder than actually working on the assignment itself. I think an easy way to fix this is to clearly layout the tips to get pass the autograder, rather than having the students to search through the forum for a solution.
By Joseph D P•
I feel like the assignments for this class were very lacking compared to the other courses in this specialization. They were glorified code copy and pasting and didn't make you learn much. There was much more video instruction than in the other courses in this specialization, though. Definitely would recommend reading the accompanying O'Reily book to help you understand the difficult concepts better.
By Eric M•
I learned a lot from this course, but I do not feel like I truly understand everything. There was an extraordinary amount of information that made it difficult to keep on track and take everything in, not to mention apply the concepts in the assignments. I feel confident with the concepts and I could do much better in the future with more practice with skills developed from this course.
By Anand M•
The course is good but I expected a faster response for my regarding assignments & course materials .
Can you guys make sure that your mentors reply faster to student queries ?
You need to make assignments more descriptive as lot of time is being spent on forums to just understand the problem correctly.
The autograder behaves erratically lot of times so you need to make it more efficient.
By Sourav P•
Nothing wrong really. Should have provided more mathematical theory in the resources section.
Assignments should be a lot tougher and on real life data sets which require recodings and transformations. Quizzes should be more relevant to the lessons taught. More hardcore theoretical resources, like books and research papers should be included in order to complement the practical lessons.
By Muhammad H R•
This course was too theoretical and lacked any practical exercises that would help me solve any problems. The professor went too deep into the concept and in the end you were left wondering what is the purpose of the algorithm. Seems as if they were concerned in covering a specific amount of topics rather than making the concept of machine learning more approachable.
By Fatemeh M•
First I want to thank all the instructors and anybody that was involved in this course preparation. That was a great opportunity and I really liked that but not in all parts . I think the syllabus was a little heavy and somehow I couldn't follow that . in the programming part I needed more guide and sample .
But in general It was good and I thank you so much.
By Robert S•
The subject matter is interesting, but there are many issues with the assignments that should have been fixed before the course is offered, for example, unworkable code segments that remain in the assignments or that prevent the grader from functioning properly. Be sure to read the forum carefully before beginning coding assignments.
By Mario P•
I struggled with this course. The lectures cover a great deal of information extremely fast. I appreciate that there are more lectures than in previous courses in the specialization and the information is better presented IMHO. The assignments were quite difficult and I struggled. Relying heavily on discussion forums and online posts.
By Vatsal K•
I think the instructor must give more practical explanation for scikit-learn. I need to research almost everything for completing a particular assignment. Please have changes in pitch of your voice while delivering the lectures so the lectures don't seem boring. Also, please update the autograder !
Overall a good course. Thank you.
By MD T R J•
The course material is good, but the teaching style is too boring. Without the standstill slides, if there is animation, it would be helpful for us. And, the assignments are not straight-forward and the autograder is buggy. As an example, I can run the assignments easily in the jupyter, but the autograder faces problems.
By Jun L•
There are too many errors in the video and even in the quizzes and assignments which will affect the final grade and wastes studying time to figure out it is an error. It is pointed out in the discussion forums but no one is taking the action to correct it. Moreover, at least 3 of the reading materials fail to be loaded.
By Ishan D•
Good course for beginners. However, things like feature selection, dealing with null values, model selection should be in depth and an end to end example on a real world dataset should be explained step by step to with best practices to develop learner's interest towards picking up problems and solving on their own.
By devansh v•
Course is good but leaves a lot of things unexplained and feels like the weeks explaining ml algorithms are in a rush.But the assignments are truly remarkable.I would recommend this course to anyone who already knows machine learning and would want to apply it on some good problems/assignments before Kaggle.
By Alexey F•
I really like the main idea of this course, i.e., using sklearn lib along with basic lectures on the ML topic. So, I was expecting that we will be following the contents of text book by A.C. Müller & S. Guido. In the first two weeks it was really good. The materials of last two weeks were quite compressed.
By Oscar F R P•
Its a really complex topic an though videos seem long enough to explain some ascpetcs of it, many little things go under the radar and make it difficult to understand some thing. Algo, the lectures are a bit weird since the professor sometimes stutter or changes ideas mid sentence.