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

4.6
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Оценки: 6,556
Рецензии: 1,172

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

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

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!!

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876–900 из 1,156 отзывов о курсе Applied Machine Learning in Python

автор: YYuan

Nov 28, 2019

This course involves lots of concepts and algorithms in machine learning. As it is said by the teacher, for time, effort and aim limitations, this course only involves basic concepts and usage of sci-kit learn. It is a good hand-on course for beginners. Assignments are not so challenging compared with the previous two courses in the same specialization. I just finish assignments by following the module code in the course. I feel like not study as much as I expected through the assignment. I hope assignments can be changed by varieties and difficulties to let students know how a machine learning project is like and how the evaluation works but not simply call the precision/accuracy/recall function and the assignment finishes. Generally, you still learn a lot if you want 'applied machine learning'

автор: Guo X W

Jun 12, 2020

I personally enjoyed this course much more than the previous 2 courses in the specialisation. Overall, this course is ambitious and covers a lot of different algorithms. For each algorithm, a brief intuition is provided and we are taught how to code in Python. For this course, I felt that the assignments were a closer fit to the content covered in the videos (unlike the previous courses where the assignments required much more independent learning). However, this course will not provide the mathematical rigour that some learners may expect. Furthermore, the amount of content covered could be a bit overwhelming. It would be useful if the instructor could summarise the different steps we should take when faced with a ML problem, esp. for deciding which algorithm to use (since so many were covered)

автор: Zuha A

Sep 01, 2018

if you have a conceptual knowledge about Machine Learning algorithms, or at least supervise learning, this course would be very helpful for you. Otherwise, you are wasting your time.

This course is a programming session , helping you to implement the complicated machine learning algorithms using simple tools, without diving in any details or explain any mathematical backgrounds. So you supposed to build these fundamentals before coming here. For me, I took the wonderful course of Andrew Ng before this.

Furthermore, the course is very structured and organized, and its material, quizzes and assignments are greet , thus I consider their notebooks such a good reference I'll back to it every time I solve a ML problem.

автор: Nicolás C

Jan 15, 2018

Very good course. The content is excellent. You can get a good understanding of many popular Machine Learning algorithms. Maybe the most valuable concept you can learn is how to evaluate a classification model. It is also an applied course, so anyone more concerned of the applications than the theory will enjoy it.

The only drawback is that the evaluation of the assigments is done automatically, and you can have frustrating limitations for an answer that is correct but that is not EXACTLY as expected (I mean even the data structer have to much perfectly). The server also have quite restrictive memory limitations and the error messages are not always very helpful, but the staff will help you if you insist enough.

автор: Marcel P

Jan 28, 2019

The course is an excellent tour of machine learning methods. The best thing is that it provides the python codes for various applications of machine learning. These can represent a great starting point for real applications. The significant parameters of each model are explained and the usage of the main models is well depicted. However, the course is very dense and I think it should have been divided in 6-8 weeks. At least the unsupervised learning part, which is optional in the Week 4 should have a dedicated week, with assignments. Before doing this course I recommend something like the course of Andrew Ng (without that one, for me it would have been more difficult to follow this one).

автор: T T T

Aug 10, 2020

I found the course content ideal to start my journey in machine learning. It was a bit much for me to understand so much within so little time but now, I know where should I emphasize more and how to have more concrete idea and knowledge about ml. The course would have been the best if the content were scaled down to less and had been a bit more easygoing but people with high processing and patient brain might get the best output from this course. I did not understand a lot of things and had to self study a lot but thanks to it, I got a good start. Thanks

автор: Ian R

Nov 11, 2019

I found the course to be a little bit too much of a whirlwind for me to get much more than the broadest strokes out of it. A lot of the topics covered were mentioned very briefly without much explanation of when or how they should be applied - especially week three felt like a barrage of "this exists, this exists, this exists..." without much explanation, and I don't think I'll retain very much of it. The Week 4 assignment, however, was adequately challenging and did give me cause to go back, review and dig deeper into many of the topics covered previously.

автор: Paulo C

May 03, 2020

Overall a good course! It was really what I was looking for: main focus is on how to apply algorithms and pros and cons of each model, instead of exhaustively explaining the theory behind each one, like some others courses do. The downside was the grade system. The platform has a lot of potential, but crashes all the time and there are many errors to troubleshoot when submitting assignments. The time invested to troubleshoot these problems was really frustrating, and probably the main reason i won't continue with the specialization.

автор: Amit A

Dec 23, 2019

The course is excellent and Professor Kevyn Collins-Thompson goes to the lengths and breaths to explain various machine learning algorithms and also provides a hands-on the syntaxes for the code to provide a deeper intuition to the problem. The course has a lot of info to be digested and one must go at his/her own pace to grasp all the details. There were some issues with the grader but thanks to the excellent mentors on the decision board, they helped me sort out all the issues. So thanks to the entire team once again.

автор: yiding y

Jul 01, 2018

Pros:The course provided me with a very good introduction about Machine Learning(in Application level), for example, the relative terms that be using, differences in classification and regression models, the validation metrics and methods, the related tools using in Python. It fulfills the application goal as the Professor said in the week1. I can utilize a lot from the course into my current work. Cons: The auto-grader could be improved better which can save learners lot of time debugging it.....

автор: Lauren r

May 23, 2020

There's obviously been some reordering of videos that can be confusing and repetitive and the quizzes are not carefully worded which leads to misunderstanding of questions and answers. The material though, unlike in the two previous classes in this specialization, actually help with the assignments so that the assignments help what you learned in the classes. The material is also presented mostly at a reasonable pace (except at the beginning of the second week).

автор: Rory P

Mar 14, 2018

More detailed videos/maybe case studies on applying the algorithms in real-life jobs would be good. The assignments are generally fairly good, but can be pretty easily cribbed from the course module notebooks. While this is okay since knowing exactly what syntax to write is less important when there are a lot of examples online, it would be good to have the assignments maybe incorporate more thinking about the models and what they mean.

автор: Sonmitra M

Jan 18, 2020

The course content was good and the assignments were designed brilliantly. I learned more while completing assignments and reading discussion forums. The auto-grader should be improved, it's time wasting and frustrating experience. No response from discussion forums even on technical issues can keep you waiting for weeks unless you solve the issue by your own by reading 2- 3 years old post and meanwhile lost money, time and patience.

автор: Tesfaye G A

May 05, 2020

first of i would like to say thanks for my Almighty God for being with us all the way we do next i want to extend my thanks and appreciation to Coursera and my applied machine learing professor kevyn collins Thompson, i got this course it is very helpful for every body working on any technology apart from this i want to say a little about the course content that it was very nice if more practice added on it

thank you

автор: Vidya M S

Sep 09, 2019

Good brief explainataion of supervised algorithm , its working and how its put to use with 'sklearn' . Jupyter notebooks on each module gives you a baseline of how machine learning is done with 'sklearn'. Quiz arent bad either . May be the last assignment on the final analysis of given data to provide a prediction could have been made more challenging by including grade on the EDA and explaination of model results.

автор: Renier B

Sep 19, 2017

I enjoyed this course. Many people comment on the lack of theory, but I think as important as theory is, it is even more important to be able to practically use ML algorithms.

This course will set you up to start doing Kaggle competitions quite adequately. In fact, the final assignment is very similar to a Kaggle competition and open-ended enough to make you really feel like you need to harness what you've learned.

автор: Vinayak

Mar 02, 2019

Great course for beginners to start with Machine Learning in python. With sufficient paraphernalia about the concepts, the course dives straight into the guts of ML and helps a lot in applying ML concepts to datasets. The instructor is clear and concise and provides enough auxiliary reading for familiarizing ourself with previously-unknown ML concepts. Thanks to both U Mich and Coursera for organizing this course.

автор: Nicholas B

Feb 17, 2018

easily the most difficult course in the specialization (so far). learned a lot! Still, the course matter could've been made more clear in some areas of the assignments. Also, the time estimates are way low. Plan to spend 10 hours a week reviewing scikit learn documentation at a bare minimum. I spent over 12-15 hours a week on this course. I STRONGLY recommend if you're looking to get into machine learning.

автор: Dhanush b s

Aug 30, 2020

Many core concepts were not given much importance in the videos. The teacher talked in a very monotonous way and was literally reading from a script. Found myself going to several websites and the prescribed book most of the time.

But the final assignment really validated our work by giving us the opportunity to solve a problem all on our own without many hints.

Overall: Teacher- bad, course material-good

автор: Dawid M

Feb 24, 2020

There should be a note at the beginning of the assignment in Week 4, that we may run out of memory with the auto-grader and what to do in advance to avoid that. My biggest time in Week4 was spent looking for and upload umpteen times (trial and error) to find a memory problem instead of upload to learn to calibrate parameters. Received 0.81 (which is rather ok) in the end but the distaste remains.

автор: Vikram

Oct 17, 2017

Provide a quick and good overview of important, popular machine learning topics and their practical use with Python scikit-learn module. The material covers the important parameters to keep a watch on for performance and highlights the usual pitfalls and missteps. Very practical learning, makes one comfortable using ML tools and quickly apply for real problems like in the last assignment.

автор: Hritvik S

Jul 13, 2020

The course is designed perfectly and the pace is such that beginners in machine learning would enjoy. The course was well structured out and in a span of 4 weeks I think i learnt a lot. The only limitations i found were with the autograder not detecting files and other minor glitches like the videos not being marked completed even upon completion. But those can be fixed easily.

автор: jie

Apr 28, 2020

Just like other couses in this specialization, this course has great assignments which help alot.

As to instruction, totally different to previous courses, this instructor covered almost everything, probably too much for a four week course. I think I start to have some sense of machine learning however, I do need more study, probably Andrew Ng's course and refresh my maths.

автор: Maxwell's D

Jun 23, 2017

I really got a lot out of this course. I started with a solid background in traditional data analysis (PhD in experimental physics), but knew nothing about ML. This was a great overview, providing a just the right trade off between depth and breadth--plus it was short, which is good. I can now go and do deeper dives into the material. Thank you!