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워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
별점
12,295개의 평가
2,950개의 리뷰

강좌 소개

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....

최상위 리뷰

SZ
2016년 12월 19일

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.

PM
2019년 8월 18일

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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,861개 리뷰 중 2651~2675

교육 기관: Sah-moo K

2015년 11월 18일

Recently, I got a certification of Machine Learning course of Anderw Ng.

So the first course of Machine Learning Specialization is too easy for me.

But I think it's not a matter of how easy it is.

This program poorly explain how algorithms work

Even if the lecturers keep saying that we are going to study in detail in the later courses,

it's very difficult to stand boring situations.

And there's a serious problem.

They provide data for programming assignments, which shows different results compared to the one in the video lectures. So I am soooooooooo confused.

There are some small hardships more. But I am stopping writing this.

If at least one of the lecturers find my review, please contact me.

교육 기관: Ali Y

2019년 4월 28일

The course is completely an introduction to Machine learning and It gives you the very basics of machine learning but not in details of course! Otherwise there was no need for splitting it to 5 courses which they have canceled 2 of them. The concept parts of each week are great but unfortunately the problem is Graphlab, which you will have problems installing it on a windows and the library itself is old-fashioned and no one use it because the updated version is called Turicreate and you need to seek the docs to keep up with course in Turicreate. So i think you will be disappointed from coding parts but concepts presentations are good and gives you a nice insight,

교육 기관: Yaroslav O

2015년 12월 25일

Lectures are very easy and unnecessarily long and slow. I had to watch all of them on x2 playback speed to not die of boredom. Also, what is the point of breaking them into 3 minute chunks? Some people may need more time just for getting to the right mood to learn. I cannot imagine anyone watching 3 minute video, doing something else and returning back to it. Also, it requires me to start the next video and set the speed to x2 again.

Overall, lectures are OK and material is explained well.

Programming assignments are worthless, as they are basically "Fill one line of code that does X. By the way, here is the syntax. And here is the data to use." No thinking required.

교육 기관: Wellington P

2016년 2월 7일

The concept and overall material covered was exciting. However, the lessons often did not connect to what was actually being tested. This course requires a lot of reading of the Dato SFrame manual. If the instructors focused more on showing how to actually do some of the tested material, I would've given this course four to five stars. At the end of the day, this course does give an entry level data scientist such as myself the ability to do some 'cool' analysis, which I truly appreciated. Overall, I would recommend this course to a fellow data scientist. I just hope the instructors focus on teaching content with more focus and clarity.

교육 기관: Carin N

2019년 5월 22일

Its a fine course but most of the coding comes from the program Graph Lab, which is only free for academic purposes. So you won't be able to take your skills outside this course unless you 1) do all the HW assignments in an open-source and struggle (because there is no assistance for this method) or 2) you pay for GraphLab once you are done with the course (not worth is with all the open source packages out there). The instructors also don't make it easy for users to use the open source packages because Graph Lab splits the data differently than these other sources, making our answers always slightly off.

교육 기관: LB

2015년 11월 11일

The video lectures provide a clear and concise introduction to interesting topics in machine learning (ML). However, the exercises are very general and use 'black box' ML algorithms for most of the solutions. For me, the exercise structure was more confusing than educating. I am aware that this is the intro course to the specialization, and I am looking forward to actually building the algorithms in the future courses. Too bad you can only take the entire specialization over the course of ~6 months, and not at your own pace! Especially since the homework is checked automatically.

교육 기관: Albert V

2020년 12월 8일

This is superb introduction to Machine Learning. I've tried to read the "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" but can't understand the ideas in the book until I've finished this course. Overall, this is a great start for those who want to learn Machine Learning concept.

The downside is it uses non-standard Turicreate rather than popular Sci-kit Learn, or Tensorflow. But as they said it is more easy for a beginner to grasp the concept using Turicreate than sci-kit learn which is true.

교육 기관: Philippe N

2020년 4월 5일

The course gave a great overview of Machine Learning through case study and will help me a lot I think to design similar courses in the future. On the bad side, I have noticed the course was developed some five years ago and that the videos were not updated. The fact that for instance Graphlab changed to TuriCreate is annoying since we have videos and the notebook does not correspond to it. Furthermore, The mentors are not responsive enough on the forum. I have an unanswered question and noticed many other questions were left with no answers.

교육 기관: Varun R

2016년 1월 4일

I really liked the fact that we were given an overview of all the machine learning techniques before we actually delve deeper. However I would have rather appreciated it further if we used open source python libraries rather than graphlab!

I think the use of graphlab really did limit our scalability and use elsewhere other than on the course.

Please do consider using open source tools in further courses and also provide starter code for the assignments in one open source library in addition to the code provided using graphlab.

교육 기관: vitali m

2018년 3월 7일

Although the concepts presented in the course are interesting, all course examples are based on a proprietory python library (Graphlab) which you are most likely will never use in real life. As the course suggests you could use open source libraries (scikit for ex.) but since all examples do not use it, it will take 2-3 times more time to figure out how to do the same assignment using open source libraries. So if you hope to learn ML concepts applied to scikit, pandas, etc. that's probably not the best course for it.

교육 기관: Kelsey H

2019년 12월 31일

Very frustrating. This course is a good Machine Learning overview, and light on programming. BUT the homework is based around an opensource library, TuriCreate - this is only available for Mac OS. Windows users will have a harder time with this course.

The workaround I found was to register for a student version of GraphLab (which the course previously used). I used an older version of Anaconda that I got from the GraphLab website, and modified the homework assignments to use GraphLab instead of TuriCreate

교육 기관: Pier L L

2016년 8월 10일

Nice overview of the specialization. Since it aims at showing the advanced and interesting things you will learn during the specialization, some of the practical sessions are way too advanced. Thus, for me felt more like a mechanical copying of what the instructors did rather than an actual assessment of what I understood. Also, since some of the applications are actually repeated at the beginning of the main courses, it feels like a repetition somehow when then you move to the specialized courses.

교육 기관: Kevin C

2020년 10월 5일

I really enjoyed the case study approach that's why the 3 stars but I'm not gibing it a 5 because some of the videos could just be skipped because half of them are the instructors laughing and the other half is some important info. Also it looks like they don't really care about the community because not all questions asked in the forums get answers. Finally, there are some clear mistakes in the Quizzes that haven't been resolved although many people have complained in the forums.

교육 기관: Andrey B

2016년 6월 4일

The course could have been marked by 5 stars if it weren't for the promotion of a commercial Python library developed by one of the speakers. There is no way a student could complete the course without having Python installed and a free licence acquired from dato.com.

Students should be able to use any programming languages and scientific libraries to do their homework and the subsequent courses of the "Machine Learning" specialisation are excellent examples of such approach.

교육 기관: Jakub V

2018년 9월 1일

I was unable to get graphlab running – had to use turicreate instead. Also, the most interesting part, deep features, came a bit "ex machina" – without a proper explanation how to create what was prepared. Also, I really miss the parts 5-6 of the specialization which look very interesting. The basics are already well covered at many places. If the parts 5-6 were existent, I would probably take the whole specialization. This way, I will pass.

교육 기관: Christopher O

2016년 11월 7일

I enjoyed the course and I will continue with the specialization. I am giving a 3-star rating as i) the lectures need to be updated with correct data or need to provide guidance as to when one should expect individual difference when following along with the notebook, ii) instructor / mentor response in the discussion forums is lacking, iii) graphlab is now an outdated tool as it is not commercially available.

교육 기관: Konrad Z

2017년 8월 14일

It would be better for the course to focus on using scikit-learn for machine learning. The course focuses on using GraphLab (https://turi.com/download/academic.html), which is a commericial product, free for academic use. I'm doing this course for professional purposes and my preference is to gain familiarity with free/open source solutions that I will be later able to utilise in production environment.

교육 기관: XingliangZhao

2019년 12월 24일

To be honest, this course is not friendly to windows 10 users because it forces students to use the apple Inc's Turicreate instead of the most popular sklearn. Admittedly, windows 10 users can still install the Turicreate by WSL but not everyone wants to add a subsystem to their windows just for this course. Except for this, this course has a nice structure and the content is really practice-oriented.

교육 기관: Chris T

2017년 1월 18일

I found the Course very interesting, well prepared from the Tutors and I liked the case study Approach since it provides actual examples where Machine Learning can be realized. I am interested to enroll in the second Course of the certificate to validate if it will go into more Details and Background regarding the build of the algorithms theoretically and in Python. I would like to thank both Tutor

교육 기관: Manuel O

2016년 8월 31일

While I am aware that this is an overview of the other courses in the specialization, I felt that the quizzes and programming exercises didn't really get into the actual topic. For example the recommender systems quiz and programming assignment have nothing about factorization except a single superficial question. The material is clear and the overview is nice, but the practical part let me down.

교육 기관: Jess T

2017년 8월 29일

A nice ML overview that introduces many tools without going into detail on how they work. Pro: Loved the programming assignments, nice Jupyter notebooks. Con: found the constant hyping of the Capstone course (which got cancelled) frustrating. The GraphLabCreate software was neat to see and easy to use, but ultimately I preferred the more first principles approach of Andrew Ng.'s ML intro course.

교육 기관: Dheeraj A

2016년 11월 27일

This is a good introductory course, however the quiz questions and over dependence of graphlab are off putting. The instructors share good insights about the need and motivation for various ML techniques. I wish there was more support on the project using pandas and sklearn. Graphlab is immensely powerful, however not adopted in industry making it hard to apply the learning in real world.

교육 기관: Christopher W

2016년 3월 6일

Pretty high level overview. I guess it's necessary to give a roadmap for where the concentration leads, but I wonder if each lesson couldn't have been added in its respective module, or if at least the Foundations Module couldn't be shortened a little - or alternatively made a bit more challenging. I'm on the first real module now and the change in difficulty is quite significant.

교육 기관: Sander v d O

2016년 4월 1일

This course is for you if you really don't know anything about Machine Learning and nothing about Python. If you do know something about it, look for a different course.

I learned the most from lesson 5 and 6 about recommenders and deep learning because I knew nothing about these subjects.

The programming exercises are disappointing: just cut and paste. I found this demotivating.

교육 기관: Sean I

2017년 11월 5일

I wish they used open source tools for this. I will not be paying for a GraphLab account nor do I see myself using it in the future. I felt less inclined to strain over learning the API and was unused by the technologies. Other than that the course is pretty interesting as I was able to apply some cool data analysis using ML practices I've learned in other Coursera courses.