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2,862 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

PM
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.

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.

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2626 - 2650 von 2,774 Bewertungen für Machine Learning Foundations: A Case Study Approach

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

7. 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

27. 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

31. 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 E 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...

von Tom v S

5. Juni 2018

In and of itself, the content of the course was pretty good. However, after working through 2 deep dive AI courses of each 6 months, obviously this particular course was not much of a challenge.

von Diego A

24. Okt. 2016

The Professors and the lectures were excellent. Homeworks are way to easy. Would like to use open source tools like pandas and sci-kit learn instead of proprietary tools like graphlab.

von Neelam

18. Mai 2020

I cannot download all the software needed specifically Turicreate, despite the provided link it shows never-ending errors, after a week of trying I had to give up the course since.

von Kenny J

21. Mai 2020

This course needs to be updated. It's hard to follow the notebooks since the lecture was on GraphLab, and some of the explanations were not elaborate enough, especially Week 6.

von Zein S

17. Jan. 2018

I like more to work with sklearn rather than GraphLab..

Actually many recommended this course to me, and I expect more excitement in the next courses in this specialization

von Eric.Wang

10. März 2016

I don't like this course , because the homework can not match the lesson. I can not got more messages to completed the homework.

So I will Unregister this courser , Thanks.

von Morteza M

20. Nov. 2016

The only reason that I am giving 3 star is the design of the quizzes for each week. The readings are too long and the content of the quiz sometimes gets you frustrated!

von Chih W L

19. Sep. 2016

Professors are very good , i am really enjoy in this class, but no further discussion about implementing ML algorithm, just call the API to handle the sort of data.

von Zhongyi T

9. März 2016

The lectures are fine. However the content is way too easy. Another course on Coursera `Mining Massive DataSets` is much better, in the depth and horizon.

von Fabio

7. Okt. 2018

App needed to complete assignments ceased to function early on - forum / admin did not help to find solution. Otherwise good intro to get started with ML.

von Deleted A

5. Juni 2016

Generally ok. Towards the end of the course, the lectures could have been a bit more in depth - or provide students with a more in depth reading list.

von Kai W

21. Nov. 2015

I think this is an excellent course. I would have given 5 stars if this course is not based on Graphlab which is not affordable to the general public.

von Murat O

28. Jan. 2016

Gives a really broad overview of ML concepts. Examples (and assignments) use a commercial Dato product called (GraphLab Create). Expect nothing else.