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Bewertung und Feedback des Lernenden für Machine Learning Foundations: A Case Study Approach von University of Washington

13,026 Bewertungen
3,099 Bewertungen

Über den Kurs

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....



18. Aug. 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.


16. Okt. 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

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2901 - 2925 von 3,024 Bewertungen für Machine Learning Foundations: A Case Study Approach


15. Juni 2020

Installing software parts gave me a very hard time.

von Bastian M P

1. Juni 2016

Could go a little more in detail on the algorithms.

von Jaime O

31. Jan. 2017

The Deep Learning part needs to be improved

von Chen S

26. Okt. 2015

Very basic, the quizzes aren't clear enough

von Li-Pu C

29. Okt. 2020

A little bit too easy, but good for rookie

von Harsh V K

8. Mai 2019

Should use Python 3 instead of Python 2

von Phú L L H

3. Apr. 2021

sofware guideline is quiet useless

von Yu G

7. Feb. 2021

No idea what to write here...

von Jorge C

29. Mai 2016

It is a very simple course.

von Ricardo S

10. Aug. 2021

Feels a bit out dated


25. Juni 2020

Good for knowledge

von Fredick A S

6. Apr. 2018

No python..

von Nasimul J F

16. Aug. 2020


von Kai C

24. Nov. 2015

Too easy

von Geetha G

16. Aug. 2021


von Anshu R

12. Sep. 2020


von sakthivel

4. Sep. 2020


von Shahriar K S

23. Aug. 2020


von tarun v s n

23. Juli 2020


von Abhinav S

10. Mai 2020


von Bindra B

21. Juni 2021


von CHEE W M

26. Sep. 2019


von Andrew S

3. Dez. 2016

The content of this course is interesting, I liked the examples, and the material gave an interesting overview of different aspects of machine learning. From that perspective, the course is as advertised. But, where this course goes wrong is value for money - it is very superficial and not worth what is charged.

As noted by others, this is not a course for learning so much as an advertisement for the instructor's own pay software and their other Coursera courses. I'm not against that per say if it was entirely free, but charging for an advertisement is ridiculous. In my case I thankfully started with the free model so I didn't lose out, but I could see others being dissapointed. I strongly recommend starting the material with a free signup and only pay if you really want the extra grading.

My other main problem was with the pace and detail in the course. I would have liked more detail, but I recognize this was intended to be a high level view so I'll live with that level of detail. The material covered, however, does not need 6 weeks worth of lectures. This course could be ~1/2 as long, cover the same material, and be a MUCH better course.

Other small problems include some poorly edit videos (there are a lot of examples of simple stumbling in the videos that should have meant they do another take), very short videos (maybe a person preference, but the number of <2 minute videos here is annoying, especially when there's a 5-second standard video at the start and end of all videos). All in all, there's just a lot of wasted time.

When signing up for this course I was really excited for the entire specialization - now, not so much. I'll probably try the second course in the series (for free to start) to see if things improve, but ironically this advertisement video has if anything turned me off their other products.

von Jean T

17. Apr. 2017


(1) I feel I spent most of the time learning graphlab. Suggest replace it with standard Python as the standard tool for this class. Provide any needed additional code in standard Python.

(2) Course is better in the front end than in the back end.

(3) Week #6 is significantly more involved than previous weeks. Suggest divide Week 6 into two sessions: Neural Network and Nearest Neighbor applying neural network results (ImageNet 2012 was mentioned and not explained. Therefore the Nearest Neighbor homework assignment from the student's perspective does not have much to do with neural network other than using the results from ImageNet 2012, which was not explained in any detail anyway). This will allow more time to delve into the forward and backward propagation which should have been explained in more details.

(4) Home assignments are not best worded, especially homework assignment for Week 6. Suggest reword in shorter statements that are more to the point.

(5) Programming presentation and assignments can seem like exercise in graphlab and SFrame functions rather than machine learning.


(1) Class presentation by Professor Fox on recommender system is detailed and clear.

(2) Classifier block diagram shown by Professor Guestrin is good, clearly distinguishing training the classifier and the subsequent use of the classification (prediction).

(3) Neural network quiz in Week 6 is excellent. It drills down on the multi-dimensional space that neural network is particularly good for.

von Faik N

14. Jan. 2022

A​ lot of good info but the code they used wasn't the same as the code in the notes (e.g. Graphlab vs Turicreate) which was confusing. In the last quiz they had us build models to find the knn cat to a specific cat photo that was supposed to be the first in the image dataset. Unfortunately that photo was no longer in the dataset so I did the code just to learn it, then I had to eyeball guess which of the quiz options matched their cat photo the most closely. In the fourth quiz we were expected to do coding that was never presented in lecture, and the documentation they linked to was bad. With regular python documentation I'm able to learn how to code in new ways because they give examples. Not so with the turicreate documentation. Another issue was that in one of the quizzes they threw in the vaguery of normalizing to either 0 or 1 in a neural net logic gate question. I found two correct answers where one normalized to 0 and the other to 1 yet was still marked incorrect. Online courses will eventually replace in-person classes but not when they're so filled with errors and students are unable to clarify these errors with faculty. The learning process itself can't progress efficiently because it's unclear if we're wrong because we require more learning or because the information presented has errors or is in a different format. The most frustrating class I've ever taken.