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Machine Learning Foundations: A Case Study Approach(으)로 돌아가기

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

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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


2019년 8월 18일

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.


2016년 10월 16일

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

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 3,063개 리뷰 중 76~100

교육 기관: Peter F

2020년 3월 30일

This course would be okay if it weren't for turicreate, a Python package that's supposed to simplify things. If you have Linux or a Mac, it will do just that, but if you have Windows steer well clear of this course. The lecturers haven't considered the possibility that anyone might not have Linux or a Mac. All the faffing around getting turicreate to work (I did it once and I'm not doing it again) wasn't worth my trouble so I ended up guessing the answers to the quiz questions (you're allowed three attempts every eight hours) just to get this course out of the way. I'll use something actually accessible for the remaining courses, namely R.

교육 기관: David Y

2021년 10월 3일

Content is not updated, 3 years old, they tell you that the course will cover TuriCreate but all the videos show GraphLab content. Syntax is for Python 2, and although this last one is not supercomplicated it shows the lack of interest for the students from the University because after 3 years they haven't updated the content!

Go to the forums and see how many people are stuck in Week 1, just trying to install the tools requested, which require plenty of workarounds to be installed in windows.

교육 기관: Rithik S

2020년 5월 26일

The files that are given in readings are unable to open and turicreate cannot read that files also. I cant complete my assignments without reading those files. They haven't given any detailed explanation about how to read those files. In videos they had explained through csv files but in assignments they had given sframe file which are unable to read

교육 기관: Yakubu A

2020년 12월 23일

The learning tools and environment is not friendly. The use of graph lab seem outdated since python 3.7 does not seem to support the module. I suggest the course be reviewed. Python 2.7 seem to be going out of the system so something should be done about this

교육 기관: ye

2021년 1월 31일

The course is limited to use special package - turicreate, sframe, no detailed explanation of how to install that. Packages used are very out dated

교육 기관: Jitendra S

2016년 4월 29일

Dato tool does not even install properly.. so n´makes no sense to continue with the course. The support team fail to help in installing ... :-(

교육 기관: Ashutosh N

2020년 5월 30일

The course is explained using turicreate , which does not work in windows properly. It should have been explained using open source libraries.

교육 기관: Krupesh A

2019년 2월 15일

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

교육 기관: Rolando J R I

2022년 2월 14일

They are using python 2, It is very out-of-date.

After the first week, I count not pass the first test...

교육 기관: Shreyash N S

2020년 5월 20일

graphlabcreate creates many problem while should be changed

교육 기관: Japman S

2020년 6월 6일

Based on Python 2 libraries not working on python 3. Obsolete Course

교육 기관: YM C

2019년 9월 6일

Too old, bad packages, not much to learn. too basic.

교육 기관: Darren R

2015년 10월 13일

Thoroughly disappointed to see this course based on

교육 기관: Kaushik M

2016년 5월 1일

Too many videos and not cluttered assignment codes

교육 기관: D. F

2021년 2월 2일

Out of date material. Poor instruction

교육 기관: Rohit

2020년 4월 19일

This course is pretty good for beginners. All domains are explained briefly as an introduction. The best part about this course is very good hands-on sessions which are really helpful to understand concepts. The course is not very detailed but it's very good to start with. Looking forward to quality courses ahead in this specialization.

교육 기관: Shibhikkiran D

2019년 4월 13일

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.

교육 기관: Diogo P

2016년 2월 15일

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

교육 기관: Karthik M

2018년 12월 27일

A good course to understand the basics of Machine Learning. The only issue is the use of Graphlab library. Since it only works on Python 2.7, it is not convenient for people who prefer Python 3

교육 기관: Alexandru B

2016년 1월 21일

Great course. Very informative and inspirational. I got tons of ideas from it! Thank you

교육 기관: Mallikarjuna R V

2019년 1월 17일

Wonderful opportunity to learn and execute hands on coding of Machine Learning. The amazing task that Machine Learning methods and algorithms does behind scene is understood for the following cases / intelligent applications:

1. Regression (e.g. Predicting House Price etc.)

2. Classification (e.g. Product review sentiment, Spam detection, Medical diagnosis etc.)

3. Clustering and Similarity (e.g. Grouping news articles)

4. Recommender (e.g. Amazon personalized product recommendations, Netflix personalized Movie recommendations etc.)

5. Deep Learning and Deep Features (e.g. Google image search, Image-based filtering etc.)

The main challenge for me was to code using “Python3, Pandas and SciKit-Learn” instead of “Python2, GraphLab Create and SFrame”. I am now confident to develop intelligent applications based on Machine Learning. Thanks to Professors (Emily and Carlos) and to Ashok Leyland-HR for giving me this opportunity.

교육 기관: Sundar R

2020년 8월 19일

The teaching is of good quality and the lectures are easy to follow along. The only downside I thought was week 6 where I felt the topics weren't covered in enough detail in order to clear the quiz. Lastly, very disappointed by the exclusion of courses 5 and 6 which would've made this specialization a complete package.

교육 기관: akashkr1498

2019년 1월 18일

lacture was good but one point i want to share to you don't use rare tools for assignment personally i faced lots of problem while installing graphlab better to switch to some common tools like sklearn python platform .

교육 기관: Yuvraj S

2019년 2월 1일

It is a good course if we take into account the foundational part. But since only one library has been used to solve the issues, one does not explore and write their own functions.

교육 기관: Sijith K

2021년 8월 5일

The course is good. Very well taught. The issue is with Turicreate. Im not sure why we have to use Turicreate. Looks like its being promoted. Thats ok, but the real issue is installing it. I lost nearly a week on that. To figure out what to do and how to use it. It was so frusterating to start the course like this. But then, I found a solution in the discussion forum by Thet oo Zin, who has converted all the SFrames that can be used in Google Colab. That was helpful.

Then, many commands are in Turicreate. After this course I have to go figure out how those commands are written outside of Turicreate. For example:

1 - Load_Data = turicreate.SFrame('path of dataset')

2 - knn_model = turicreate.nearest_neighbors.create(x, y, z)

But the course is amazing, interesting and easily understandable for a newbee to python/ML like me.