Chevron Left
返回到 机器学习基础:案例研究

學生對 华盛顿大学 提供的 机器学习基础:案例研究 的評價和反饋

4.6
12,275 個評分
2,941 條評論

課程概述

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....

熱門審閱

BL
2016年10月16日

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

SZ
2016年12月19日

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.

篩選依據:

2726 - 机器学习基础:案例研究 的 2750 個評論(共 2,853 個)

創建者 Yuliana F N

2020年12月22日

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

創建者 Ajay S

2019年3月4日

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

創建者 Serban C S

2018年2月11日

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

創建者 Pēteris K

2017年9月23日

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

創建者 Luca

2016年11月10日

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

創建者 Pubudu W

2017年7月10日

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

創建者 Nguyễn T T

2015年10月13日

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

創建者 ADNAN A G

2020年10月9日

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

創建者 Neb T

2017年6月7日

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

But they were practical.

創建者 Thomas M G

2018年2月21日

In my view, too much focus on GraphLab.

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

創建者 Zizhen W

2016年10月16日

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

創建者 Rajdeep G

2020年9月7日

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

創建者 adam h

2016年2月8日

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

創建者 Cameron B

2016年4月20日

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

創建者 Uday k

2017年5月1日

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

創建者 Alexander B

2015年11月4日

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

創建者 Naveen M N S

2016年2月7日

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

創建者 Carlos A C L

2021年1月25日

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

創建者 Saket D

2018年2月28日

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

創建者 kaushik g

2018年3月25日

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

創建者 amin s

2019年5月29日

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

創建者 Rajiv K

2020年6月20日

Have to improve for other environment.

have to explain other alternative too.

創建者 Vamshi S G

2020年6月27日

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

創建者 Julien F

2017年11月16日

Some quiz questions were vague and/or ambiguous, or not covered in talks.

創建者 Marco M

2015年12月4日

Too much synthetic on very important parts, too much focused on graphlab