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

4.6
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Оценки: 6,151
Рецензии: 1,107

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

FL

Oct 14, 2017

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!!

OA

Sep 09, 2017

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

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51–75 из 1,089 отзывов о курсе Applied Machine Learning in Python

автор: Andriy A

Aug 13, 2019

A lot of bugs in Assignments. Instead of learning ML need to go though forums/code to fix simple bugs like files locations readonly/ etc

автор: Rezoanoor/CS/Rezoanoor R

Mar 22, 2020

Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

автор: Omid

Sep 22, 2018

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

автор: Ipsita D

Apr 20, 2019

No visible support from groups forum. Videos knowledge is limited to complete assignment or quiz.

автор: Shaoqi C

Mar 10, 2020

This is my worst experience of submitting assignment and I found out that I'm not alone

автор: Sandeep S

Nov 24, 2019

I am not happy with the course material and the way teachers are teaching.

автор: Nomthandazo T

Mar 03, 2020

this is the worst course ever. so bored and frustrated

автор: Edward G

Jan 25, 2018

Terrible quiz problems and grading mechanism

автор: kapish s

May 28, 2019

no teacher intraction

автор: Harshith S

Jun 19, 2019

Dude

автор: Kevin L

Jun 25, 2017

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)

автор: Luis G A B

Apr 12, 2019

Muy agradecido, mis felicitaciones al Profesor Collins-Thompson, se muestra como una persona amable, dinámica y con alto grado de conocimiento, gracias a sus enseñanzas estoy aprendiendo más sobre el proceso de machine learning, siento que aun me falta mucho por recorrer, sin embargo, a lo largo de este curso aprendí los métodos, tipos de modelos, herramientas tanto para clasificación como regresión enfocándome en el área. De igual forma la literatura es muy interesante, se encuentran artículos que al leerlos vas comprendiendo como ha sido el proceso de transformación en este campo y gracias a esto, se me han ocurrido ideas que me gustaría compartir o estructurar para evidenciarlas de manera mas formal.

Muchas gracias por el apoyo, gracias por las observaciones y anotaciones dentro de los foros de discusión, siento que puedo seguir aprendiendo mas y es por eso que estoy agradecido por mis conocimientos adquiridos, los cuales siempre puedo retroalimentar viendo el curso nuevamente cada vez que lo considere pertinente.

автор: Stephen K

Oct 03, 2019

5 starts for content. The lecturer and slides were good. The assignments were often difficult and took many hours longer than the stated 3-4 hours. Assignment 4 was particularly heavy in time. I finished the course feeling equipped and confident enough to take on straightforward machine learning projects from start to finish. I've dropped a star because the autograder uses an older version of Python and older libraries, which meant I had to spend around 8 hours re-engineering my *correct* code to conform to old libraries.

Addendum: I've uprated the course to 5 stars after having just completed the fifth, optional week on unsupervised learning. It's unassessed but does give a nice introduction to the subject. Thanks!

автор: Jack O

Apr 25, 2018

Though I would have liked a bit more insight into the actual algorithms behind machine learning, this class did a great job of giving us problems and forcing us to be resourceful and hunt down the answers, whether via course forums, Stack Overflow or other random Googling. We were exposed to a ton of different algorithms and libraries, and we got to experience the whole spectrum of data science: data importing, cleaning, exploratory analysis, feature selection, model selection, parameter tweaking and even some visualization. It was a lot of fun: challenging at times, but oh so rewarding in the end!

автор: Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

автор: Bart T C

Oct 10, 2018

This course is excellent. It contains a great deal of instruction each week (1-2 hours), and it also has many supplemental references for people who want to go deeper. The quizzes are actually very challenging, and require study of the material. The assignments were easier for me than the other courses in this specialization, but they were focused on application of the material to real world problem, which is the purpose of the course. The final assignment is very instructive and challenging. The instructor is very knowledgeable, and teaches in a thorough, but easy to follow, manner.

автор: Tsz W K

Jun 09, 2017

I completed the Machine Learning Specialization Certificate before taking this course. This course is an excellent applied course that quickly gets into the key aspects of using sklearn. This course is ideal for both new learners and experienced learners who just want to learn more/revise about machine learning. For the final assignment, it requires substantial data cleaning techniques covered in Course 1 in this specialisation. Overall, I feel very comfortable with using Python for any reasonable size of machine learning problems after taking this course.

автор: Guenael S

Jan 20, 2018

The class provides a perfect introduction to the scikit-learn Python module. The videos are engaging and insightful. The quizzes are challenging while not requiring too much time writing out solutions (it does take time finding some of the more subtle answers, by reviewing details in the videos). The executable modules are perfect to bootstrap machine learning projects. Homework assignments can get complicated, and you should be familiar with advanced data structure manipulation in pandas and numpy to make progress. Assignment grading is very well done.

автор: Anad K

Mar 28, 2018

Excellent course for Machine Leaning. Discusses wide range of Supervised machine learning and gives a very brief introduction on Clustering algorithms(Unsupervised). Users can immediately put to use the knowledge gained during the course.

Some more briefing about feature transformation and other such elements can be included in the course material to make it better. Also unsupervised machine learning could have been included with grater depth. Overall this course is highly recommended to aspirants interested in ML with some python knowledge.

автор: Matt R C

Feb 15, 2018

The course was very well prepared and the instructor presented the material clearly and informatively. I've seen some courses where you spend more time trying to understand and keep up with the instructor. In this instance, this was not the case and you could spend more time understanding the material. The instructor spoke slowly and clearly.

I do have to say I purchased the corresponding book as recommended but I didn't feel it was necessary. Good book, I just think the material in the course was presented well enough on its own.

автор: Ankur C

Nov 13, 2019

Great course for Machine Learning Algos. This series of lectures also helped me in understanding two beginners books for ML -

1. Introduction to Machine Learning

2. Hands on to Machine Learning.

Professor taught in a very informative and easy to understand way. Really thankful to the professor. Each and every algo is well explained with strengths, weaknesses.

questions in Quiz are very good these were not so easy and not so tough.

I will recommend this course if you want to learn ML using Python.

Thanks a lot, sir.

автор: Zhu L

Oct 23, 2017

The course is very well-designed, with the first three weeks learning basic know-hows of all the tools we need, and the fourth week make full use of every model we've learned.

Even people with no prior CS background can get along well enough.

Getting 100/100 out of the final problem is actually a passing grade, very easy if you use what you've learned so far the right way.

When you're willing to spend more time exploring the models, methods and parameters, the reward will be worth your efforts.

автор: Refik E

Sep 20, 2017

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

автор: Tony K

Jun 05, 2020

A solid course. The help found in the forums was also way more useful than the first course in this series. While course two was generically useful, this third course was technically useful. A very good introduction into sklearn. The video instructor/professor was also very clear and methodical in presentation. The assistance by the class monitors was leaps and bounds more useful in this course than course one (I almost quit after course one because of it, so glad I didn't!)

автор: Krishna C P

Jul 04, 2017

excellent course for following reasons:

1. Excellent i python note books. What ever a student must know is kept in it.

2. every topic is explained simply and well upto what ever we need to know.

3. if you are not in academic field(not planning to do phd on this stuff). Trust me how ever advanced courses you do but after a week or month. these are the points which one need to remember.

4. Course and programming labs are in perfect sync.

Thank you very much for keeping this course