Chevron Left
Zurück zu Machine Learning Foundations: A Case Study Approach

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

4.6
Sterne
11,890 Bewertungen
2,846 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....

Top-Bewertungen

SZ
19. Dez. 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

BL
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

Filtern nach:

2601 - 2625 von 2,760 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Romain R

10. Apr. 2017

The content is really good, well explained=> 5 stars, nothing to add.

Why the 3 stars then ? Graphlab. If you use the pydata stack, as it is said to be possible in every assignments, you get stuck on the quizz due to variations on data and the algorithm used, so you can't really get quite the same answers.

von Vivian Y Q

31. Mai 2018

Videos were too short to go into details. Too much reliance on the package they development themselves, though I appreciated the simplicity, I don't get to learn about a lot of technical details. So you know how to run a image retrieval model without knowing what are the deep features, for example.

von Troy D

5. Feb. 2020

Good course, learned a lot of basics. I think this course is rather old though and getting a lot of the required software up and running required a lot of work since there are much newer versions available now. I found that I had to do a little extra to get the older packages working in Jupiter.

von Aleksandar S

25. Mai 2016

The course content is great. It gives overview on what is going to be learned in details in the next courses. Considering that it is an introductory course and the fact that it utilizes the GraphLab library as tool, I believe it is overpriced compared to the other courses of the specialization.

von Yaniv S

15. Jan. 2017

The whole eco-system is based on Graphlab create which is not very commonly used in the industry. The "Programming assignments" are very much like the exercise done in the videos - so no real thought and effort were needed. The Deep learning part is really bad thought and bad examined.

von Eric J

12. Juli 2016

The enthusiasm of the instructors was the best thing about this class. But I really wanted a more rigorous methodology - and didn't really get it here. But it was an alright introduction to machine learning but not enough if you want to know what makes the 'black box' work.

von Paulo S B d O F

5. Sep. 2016

Pros:

(1) Teachers know what they are talking.

(2) They are energetic and funny.

Cons:

(1) The course uses proprietary and expensive tool.

(2) The course is too simplistic.

(3) The teachers, although they know what they are talking about, they aren't very good at teaching.

von David K

1. März 2016

I think that the course is redundant, it is to general, trying to capture to much, and using a commercial program tool that's doing to much behind the scene.

The second course in the specialization is really great though and you wont miss anything if you skip ahead

von Varun J

24. Sep. 2015

A lot of problems with software installations. But, the professors for this class seem to be very passionate about the course and they teach well. If not for a lot of problems faced during software installations(which is still not resolved), would have given 5 stars

von Michael C

10. Apr. 2016

Really just an overview of the topics to be explained in detail afterwards.

Big plus for the use of python + notebooks but otherwise, if one is interested just in the overview and not in all the specialization, maybe the Andrew NG course is more detailed.

von Bernardo C

8. Juni 2016

El curso tiene mucho potencial, pero hay que afinarlo.

Pienso que los vídeos deben ser reeditados. Tienen errores y conceptos confusos. Deberían ser tan claros como para lograr tomar buenos apuntes y usarlos en las tareas. Las tareas son casi mecánicas.

von Rishi H

11. Juni 2019

Content and material is good and the trainers are good. Only issue i found is course assignments are heavily dependent on Sframes and graphlab which does not work most of the times.,they should go with panda libraries which is easily accessible.

von Aman S

14. Juni 2018

The worst thing about this course is graphlab. Trying to run it since last 10 days with the help of every available online resources, but in vain. There are many flaws in graphlab. I tried a hundred times to view images in graphlab, but in vain.

von Juarez B

12. Jan. 2017

This course introduces the key topics of Machine Learning, but the math behind the algorithms is not explained and the programming exercises are too easy. Unfortunately, it also relies heavily on graphlab instead of using open source software.

von Mohit S S

6. Aug. 2018

Course contet is ok. But, intructors really need to teach in a platform neutral way or some other popular library for which ample support is available. In my opinion, learning a tool which is nowhere used in te industry is not a good idea.

von Tarek M s

5. Nov. 2017

the course is good for starter but according to its repetition I waited more .

one star down for many useless information in lectures about Amazon products and so on.

one star down for forcing using unpopular python library .

von Piyush K P

24. Okt. 2016

thanks to prof and cousera for this wonderful course. I wish the programming part was taught separately from basic. I have taken the previous course which was case study approach with respect to which it was slightly tough.

von Jérôme B

19. Dez. 2017

The teachers are nice and the content is pretty interesting, but they keep talking about the Capstone that we actually won't do. That make me wonder if it's worth continuing, and wonder why they cancelled it eventually.

von Gregory T

30. Okt. 2016

This was a valuable introductory survey course. For me, the challenge came from my unfamiliarity with Python not the material. I would rate this class as "entry level" for anybody with a college-level technical degree.

von Brandon P

10. März 2018

There were a lot of assumptions made about my math background. Terms and concepts were used that are foreign to most people and while the forums were helpful it was interesting to see that this is a common feeling.

von Mohammad A

22. Juli 2019

Course include great knowledge, but when coming to work on tools, they are using old method like we have python 3.7, but course is going through python 2.7 and also older version. That's creating confusion somehow

von Ivan P

6. Mai 2016

It's not a bad course, but it forces students to use GraphLab, a framework created by one the professors teaching the course, instead of using scikit-learn, a widely used framework for machine learning in Python.

von chris s

26. Jan. 2016

This course has so much potential but is based on proprietary software. The instructors are excellent and the content is really good. It would get 5 stars if it was based on all open source software.

von Nishant K

30. Okt. 2020

Great approach with basic explanation of applying and importance of the domain in read world examples. Could have been more in depth in few areas but hopefully will be taken care in following courses.

von Ahmed S A

23. Juli 2020

The course needs to be updated....I have hard times installing turicreate and graphlab on my laptop... at the end, I had to use google collab....

I guess this course needs to use tensorflow instead...