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2,896 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

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

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.

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2751 - 2775 von 2,808 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Advait S

10. Jan. 2018

While it was good for learning concepts I had real trouble with graphlab. Installation of graphlab never worked on my machine. I had to install VM just for being able to use graphlab. I really wish they had opted for more open source, free options or at least used ince such library along with graphlab.

von Ziqian G

11. Aug. 2020

There are big problems in this course, like the installation process should be given in a more specific and vivid way so that I would not have spent three days on it being a windows user...(update: still can't access jupyter notebook after trying installing ubuntu, vmware workstation, filezilla).

von Sunaad R

30. Juli 2018

Too much dependency on Graphlab package is bad. If we are learning the concept, we should reduce the size of the sample data. We should be using generic open packages, so that our learning can be easily demonstrated anywhere (especially interviews), and not dependent on graphlab.

von kunjan k

5. Nov. 2015

The case study approach is a great idea.

But I wish the instructors were more candid about the tools that were in use. It seems dodgy that the instructor is a CEO of a commercial tool vendor and is "encouraging" students to use it.

The quizzes in the course were extremely shallow.

von Raphael R

19. März 2016

The overall quality of the course is good, but in my opinion the level is quite low and there is less content then I expected. The assignments are more or less copy-paste or very repetitive. The 5-8 hour work per week are a joke, I never needed more than 2.5h per week.

von Matthew F

21. Juli 2019

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

von Joseph J F

20. Aug. 2017

It is more a course in using the tools designed by the teachers than machine learning. It might do something for a less experienced user in programming, but I didn't find it much use. The overview of Machine Learning tasks isn't bad.

von Andras H

31. Mai 2020

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

von Sunil T

24. Mai 2020

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

von Tudor S

22. Apr. 2018

The Assignments and Quiz questions are hard to read and comprehend.

Although individually the course presentations are ok, overall this course isn't a very relevant or coherent introduction to Machine Learning.

von Taylor I

11. Mai 2020

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

von Ashley

23. Juni 2019

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

von Arman A

16. Feb. 2016

The course uses proprietary tools for machine learning and data manipulation, making it effectively useless! However, the material on describing the machine learning algorithms were excellent!

von Annemarie S

24. Mai 2019

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

von charan S

16. Juli 2017

If someone is looking for ML foundations and what is ML, they can choose this course. This is very basic course and i feel should be excluded from the ML specialization.

von Eiaki M

5. März 2016

One would learn a thing or two, but the course is very sparse compared to other machine learning courses, and I didn't feel that it was worth the time and the cost.

von Robert P M

27. Okt. 2015

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.

von Evlampi H

5. Nov. 2015

The framework is ok, but it would be more insight on the functions would be much more amplifying the learning process.

Good working examples, though!

von Piotr T

6. Okt. 2015

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

von David F

2. Dez. 2015

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.

von Patryk H

14. Okt. 2015

Due to many technical issues with GraphLab lib I have to reduce acitivity in this curse for only video viewing :(.

von Elgardo E

29. Mai 2020

Course videos are outdated and requires time to investigate and research. This causes wasted time and effort.

von David H

31. Okt. 2015

Very, very high altitude introduction presented in a seemingly confused way with a lot of product placement.

von Zuozhi W

7. Feb. 2017

TBH this class's experience is not good. The lecturers seem unprepared and they talk very repetitively.

von Suhasini L

4. Sep. 2020

Not given details like what is a vector? people from non technical backgrounds will have tough time