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Вернуться к Applied Machine Learning in Python

Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
звезд
Оценки: 6,863
Рецензии: 1,242

О курсе

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

Лучшие рецензии

AS
26 нояб. 2020 г.

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL
13 окт. 2017 г.

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!!

Фильтр по:

1151–1175 из 1,223 отзывов о курсе Applied Machine Learning in Python

автор: Adithyan U

2 июля 2019 г.

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.

автор: Charles L

18 мар. 2020 г.

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!)

автор: Amit S

14 апр. 2019 г.

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.

автор: Koo H S

8 мар. 2020 г.

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.

автор: Joseph D P

14 нояб. 2017 г.

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.

автор: Eric M

29 июня 2017 г.

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.

автор: Anand M

22 июня 2020 г.

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.

автор: Sourav P

27 окт. 2018 г.

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.

автор: Muhammad H R

18 янв. 2018 г.

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.

автор: Fatemeh M

25 нояб. 2018 г.

Hi

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.

автор: Robert S

1 нояб. 2020 г.

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.

автор: Mario P

8 дек. 2019 г.

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.

автор: Vatsal K

24 мая 2020 г.

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.

автор: MD T R J

12 апр. 2020 г.

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.

автор: Jun L

7 нояб. 2019 г.

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.

автор: Ishan D

20 сент. 2020 г.

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.

автор: devansh v

17 июля 2020 г.

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.

автор: Alexey F

5 мая 2020 г.

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.

автор: Oscar F R P

17 авг. 2020 г.

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.

автор: Mohamed L M

18 сент. 2020 г.

Good explanations on videos, The only problem which was really time consuming and wasting was the problems related with the assignments submission. but overall this course helped me a lot to structure machine learning fundamentals in my mind and to get a good practice out of it.

автор: Sakina F

27 мар. 2018 г.

The videos are way too long and very monotonous. They should be cut down and reduced. The maximum length they should be is 5-6 mins other wise they becoming distracting.

The course content is good though. Quite easy to understand but going through the videos is a chore.

автор: Marcos B

12 сент. 2020 г.

I think that the subjects are very advanced. There should be a more clear specifications of prerequisites for the course. I had to look for lot of help outside the materials provided for doing the activities. The course is fine if you have the apropiate skils though.

автор: vikram m

26 авг. 2019 г.

It's a good course, but a quick one. One needs to have a beforehand knowledge of all the algorithms as they are not discussed in details. State of the art is not mentioned. Implementation and best practices are present, along with pros and cons of each algorithm

автор: Claire Z

20 июля 2019 г.

The course is quite high-level. There is nothing wrong with an applied course being high-level. The material is easy to follow, the quiz is a bit challenging but the homework assignments are quite easy to pass. I prefer a course with more fundamental details.

автор: Raymond C

27 янв. 2019 г.

The course is too tight, just 4 weeks cannot master the machine learning. This course can splitted into 2, in order to capture more on the deep learning and unsupervised learning, which are important, but being categorized as option in the course.