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Machine Learning Foundations: A Case Study Approach に戻る

ワシントン大学(University of Washington) による Machine Learning Foundations: A Case Study Approach の受講者のレビューおよびフィードバック



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....




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.



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


Machine Learning Foundations: A Case Study Approach: 2876 - 2900 / 3,043 レビュー

by Yuliana F N


Me pareció algo confusa la explicación de los modelos de recomendación, creo que debió ser más clara y y práctica.

by Ajay S


Good for beginner level, not for intermediate or advance level. I learned more about graphlab than anything else.

by Serban C S


Using a proprietary library for a paid course is not really a big issue but some people will be turned off by it.

by Pēteris K


Definitely a good intro to the richness of ML, but would have preferred more rigorous assignments and evaluation.

by Luca


not using scikit and assigment way too easy, not challenging, but high quality video, very easy to understand .

by Pubudu W


Good survey course on ML techniques. Not very detailed and the exercises are too simplistic for real learning.

by Nguyễn T T


the lectures are pretty great, engaging. the assignments stick with the lab exercise. the forum pretty active.



old and bad quality but very good explanation half of the course is programming there is no machine learning.

by Nebiyou T


Some of the modules lacked polish and have not been updated since initial recording!

But they were practical.

by Thomas M G


In my view, too much focus on GraphLab.

This is a problem because GraphLab doesn't seem to be open source.

by Zizhen W


Some instructions of the programming assignments are not all that clear, which wasted me a lot of time.

by Rajdeep G


They should upgrade the course in respect to python 3. Irrespective of that the theory part was great

by Tilo L


I​ntresting topics get broadly introduced, sadly the course it outdated at a number of occasions...

by adam h


would vastly prefer if this was taught using sckit-learn and pandas, given their broader use.

by Reem N


It is very general however it gave me an insight to different machine learning applications.

by Cameron B


The course is ok, the instruction was very poor for the deep learning section of the course.

by Uday K


The theories for the models should be explained in more detail and with few more examples.

by Alexander B


lectures were well done, but the strong focus on using graphlab ruined this course for me

by Naveen M N S


Decent course. Not very satisfied with the assignments as they are suited for graphlab

by Carlos A C L


all lectures are obsoleta, and it's neccesary to install a WSL, the rest very well.

by Saket D


Would have been great if anything compatible with python 3 was used in the course.

by kaushik g


Content was good but was few years old and things are pacing up a bit these days.

by amin s


primitive course, didn't expect this low standard from university of Washington

by Rajiv K


Have to improve for other environment.

have to explain other alternative too.

by Vamshi S G


i think the course should be updated, graphlab and some other are outdated.