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Bewertung und Feedback des Lernenden für Applied Machine Learning in Python von University of Michigan

4.6
Sterne
8,058 Bewertungen

Über den Kurs

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....

Top-Bewertungen

OA

8. Sep. 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

AS

26. Nov. 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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1076 - 1100 von 1,465 Bewertungen für Applied Machine Learning in Python

von �SADHARAN G

10. Aug. 2020

good

von RAGHUVEER S D

25. Juli 2020

good

von N. S

7. Juli 2020

good

von Arif S

1. Juli 2020

good

von parmar p

18. Mai 2020

nice

von Miriam R

26. Dez. 2019

good

von Light0617

12. Mai 2019

nice

von Shishir N

9. Jan. 2019

N

i

c

e

von Jimut B P

8. Okt. 2018

Nice

von Yi-Yang L

3. Juli 2017

Nice

von SURAJ K

23. Juni 2020

osm

von Shilpi G

2. Juni 2019

...

von M A

10. Mai 2019

ok

von PREDEEP K

24. Nov. 2018

ok

von Souvik G

23. Aug. 2021

5

von Mapitigama M D S

16. Juni 2021

s

von Andrew G

16. Mai 2019

T

von Junaid L S

14. Mai 2019

G

von Thomas

6. März 2018

g

von Oleh Z

27. Feb. 2018

G

von Piotr B

1. Juni 2017

a

von Martín J M

20. Sep. 2020

Course is excellent in content. Not heavy in mathematics (altough, I would recommend reading how models are supposed to work), the objectiv eis to have a practical understanding of how machine learning is applied and the important concepts to consider for a succesful model building. The focus is to have hand-on experience with the sklearn library.

I don't grant 5 starts (I hesitated for 4), as the course was designed back in 2018, therefore, you sometimes struggle with legacy libraries. Another issue, is that there are some hiccups when it comes to assignment uploads (for instance, the address of csv files!). As a student, this will make you hesistate and question wether the instructor screwed up with the autograder or not, which IS stressful.

Quiz 4 suddenly became non-forgiving, multiple choice answer have to be answered with 100% certainity to score full point. Quite anti-climatic, considering that previous quizes didn't work like that.

Final assignment is quite challenging, and might make the new student suffer.

I appreciate the instructors and Kevyn Collins for this great course. Now that I have a better picture, I get insights on how to focus my research efforts in sensor research and development.

von Carolyn O

19. Jan. 2020

I had no ML background, although I have the math the models are based on. The material seemed more than week's worth for a couple of weeks. The quizzes make sure you don't miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble-shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.

von YYuan

28. Nov. 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'

von Guo X W

12. Juni 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)