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

4.6
estrellas
8,013 calificaciones
1,460 reseña

Acerca del Curso

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....

Principales reseñas

FL

13 de oct. de 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!!

AS

26 de nov. de 2020

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.

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1101 - 1125 de 1,451 revisiones para Applied Machine Learning in Python

por Amit A

23 de dic. de 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.

por yiding y

1 de jul. de 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.....

por Lauren r

23 de may. de 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).

por Rory P

14 de mar. de 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.

por Sonmitra M

18 de ene. de 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.

por Thomas L

25 de nov. de 2020

Great course with the first three assignments being relatively easier and straightforward compared to the first two courses. The fourth assignment required more individual studying and comprehensive understanding of all course materials in building and evaluating prediction models. Having finished this course, I feel much more confident in my ability to work with machine learning algorithms with sklearn and panda libraries.

por Tesfaye G

5 de may. de 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

por Zaccheus S S

7 de abr. de 2021

Good overview of applied machine learning. Doesn't go too in-depth for each algorithm. Strikes a good balance which is what an "applied" course should do. However, the Jupyter Notebook content tend to have some errors which the curators might have missed. Also, the version of Python and the libraries used are outdated as at the time of me writing this review, hence in some situations I had to refer to deprecated APIs.

por Vidya M S

9 de sep. de 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.

por Renier B

19 de sep. de 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.

por Vinayak N

2 de mar. de 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.

por Nicholas B

17 de feb. de 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.

por Mohammadmoein T

2 de ene. de 2021

I did learn a lot from this course and its exercises. I believe it can be a good start for beginners in Machine learning. You might have to do lots of googling to figure out a few tricks in the assignments, but that only makes you a better learner. I wish the instructor didn't just read texts from the screen. There were a few mistakes in some of the lectures, but overall I'm very happy about my achievement.

por Dhanush b s

30 de ago. de 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

por Dawid M

24 de feb. de 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.

por Vincent R

23 de ene. de 2021

The course is a good introduction to ML. It covers lots of basic supervised ML techniques. The lecture slow pace is appropriate for presenting complex issues. It would have been beneficial to spend more time on the python case studies that are barely explained. Coursera platform issues with submitting and grading assignments should be highlighted in the assignments; not embedded in the forum.

por Vikram

17 de oct. de 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.

por HRITVIK S 1

13 de jul. de 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.

por jie

28 de abr. de 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.

por MAULANA R H 2

25 de abr. de 2022

M​ateri dan penjelasan yang diberikan selama kursus sangat bermanfaat dan mudah dipahami dengan baik, ditambah dengan pembawaan materi yang terstruktur mempermudah dalam memahami penggunaan dari setiap algoritma dan parameter yang ada. Kekurangan yang ada hanyalah di bagian penilaian assesment yang bermasalah diakibatkan dengan file yang tidak terbaca.

por Calum M

16 de may. de 2021

I learnt a lot in this course. The lectures, assessments, and reading material were all top-notch. The forums are immensely helpful. However, I'm giving 4-stars rather than 5 because I spent more time than was necessary in overcoming autograder issues. My suggestion is to improve the autograder so that assignments can be submitted more seamlessly.

por Maxwell's D

23 de jun. de 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!

por Felix H

16 de ene. de 2021

The combination of assignments and lectures worked niceley for me. Good feedback on the discussion forums, too. Only thing which should be improved is the auto grader. The course introduces a lot of algorithms, but also gives you insight into how to evaluate their performance. In the final assignment it all comes together, which is always nice :-)

por Maurizio

6 de jun. de 2019

I think it gives a great overview on Machine Learning and Sklearn. Nonetheless i noticed it is less curated compared to the prevoius courses in this specialization (wrong filenames, unfunctioning links, old version of pandas respect the one used till now). Anyway it worthed and I'll give a look also at the optional unsupervised learning part

por Çağdaş Y

22 de oct. de 2017

The teacher's voice is not motivating, it made me fall asleep all the time. But content is surely good. It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices. Seriously, speaker needs to speak more alive! I don't want to hear deep breathe noises when watching a course :)