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

8,254 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....



13. Okt. 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!!


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

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1451 - 1475 von 1,500 Bewertungen für Applied Machine Learning in Python

von Chirag S

24. Mai 2020

The content was less informative and audio quality was poor. However, assignments are fun completing.

von Rohit S

21. Mai 2020

The online grader needs to be updated as there is constant error showing up though our code is right

von Gilad A

27. Juni 2017

The last assignment was super. apart for it, the assignments and the course were too easy

von Sai P

3. Juni 2020

There were a few corrections made during the videos which ended being quite confusing.

von Philip L

31. Okt. 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

von Dileep K

3. Okt. 2021

Although content is really helpful, assignment part has many technical issues!

von Sundeep S S

4. Apr. 2021

Only classification based ML is covered. Regression based ML is non-existant.

von Iuri A N d A

4. Aug. 2021

It has potential, but the assignment evaluation had a lot to be fixed.

von Pakin P

10. Jan. 2020

How can i pass without reading discuss about problem with notebook

von Hao W

27. Aug. 2017

The homework is too easy to improve our understanding of ML

von M S V V

29. Juni 2020

Too much of information compressed within a short span.

von José D A M

21. Juni 2020

Too fast, yet too difficult. Needs deeper explanation.

von Navoneel C

21. Nov. 2017

Nice and Informative but not practically effective

von Priyanka v

8. Mai 2020

if it is more detailedthen it will be more useful

von Sameed K

15. März 2018

have to figure out a lot of things on you own.

von Andy S

4. Juni 2019

It could have been better with more examples.

von Syed S

12. Apr. 2020

The explanation could have been much better.

von Sagar J

21. März 2021

Good start but i was very boring later on.

von Jeremy D

10. Juli 2017

The topics were good, but too many were d

von Ryan S

12. Dez. 2017

Homeworks are inconvenient to submit


16. Mai 2020

The narration was a bit boring.

von shreyas

29. Juni 2020

Teacher wasn't very good

von Abir H R

30. Juni 2020

very long videos

von Wojciech G

28. Okt. 2017

To fast paced.


10. Apr. 2022