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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

9,055 個評分
2,163 個審閱


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....



Aug 19, 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.


Dec 20, 2016

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.


101 - 机器学习基础:案例研究 的 125 個評論(共 2,081 個)

創建者 Praveen B

Nov 28, 2018

The professors have taken it in a fun filled way. The material is also very interesting. This is an experience worth having.

創建者 Arindam M

Mar 29, 2019

It was a really nice course. It will be further helpful if the regression algorithms are discussed. Thank You

創建者 Hasan H J

Apr 02, 2019


創建者 Noam K

Apr 02, 2019

Nice overview, the case study approach is very useful as well as the actual python notebook assignments.

創建者 shubham k

Apr 08, 2019

this was really learning

創建者 Kunal G

Apr 10, 2019

it is a very good course if you are a newbie in this area and only know a bit of python, just be careful not to use graphlab, use turicreate instead

創建者 Sanjeev k

Jan 24, 2019


創建者 Lokesh K

Jan 27, 2019

I appreciate the effort you kept for this online course.Actually I enjoyed learning here.But you can be little bit more detailed in the ipython notebook code explanation. Otherwise ,this is the best course .

創建者 Jose E S S

Jan 13, 2019

Awesome, better course of machine learning.

創建者 Shakya S B

Dec 28, 2018

This course is very helpful for a beginner and provides a good foundation for the specialization and the advanced courses

創建者 Jungshen K

Dec 12, 2018

Very comprehensive and hand-on fashioned course, recommended!

創建者 Le N P

Jul 16, 2018

thanks instructorsthis is a great course for learning ML

創建者 XIAO N

Jul 13, 2018

I like the approach and this is a relatively easy module

創建者 Rohan C

Jul 19, 2018

Emily and Carlos made this course really enjoyable. The Case Study approach really helped with better understanding of many concepts. I highly recommend this course for beginners.

創建者 Pritesh G

Jul 20, 2018

Good material. Enjoy the Course.


Jul 28, 2018

To define how machines can learn, we need to define what we mean by “learning.” In everyday parlance, when we say learning, we mean something like “gaining knowledge by studying, experience, or being taught.”

創建者 Gerard Y

Jul 27, 2018

Very good overview, the lectures were enjoyable to follow, and brought good intuition on the topics with a good sense of what was possible. The exercises were of reasonable difficulty, and not too hard to set up, allowed to get a good feel of the potential of Turi Create.

創建者 Alfred D

Feb 09, 2018

Very good introductory course , the examples were very interesting

創建者 Tanmay G

Feb 21, 2016

Great introduction to machine learning topics

創建者 Jason J

Feb 14, 2018

I lost a week getting access to the course materials. Using the coursera iPython notebook did not work because of issues with the GraphLab key you have to individually obtain. Still I have to give this class 5 stars. Because, after that large hiccup, the material is fantastic. Emily is a great teacher and walks you by the hand through all the material. Sometimes I have to watch the videos twice, taking lots of notes, but if you put in the work, you will have a real intuitive understanding of the course material.

創建者 Paulo B M d S

Nov 15, 2017

Excelent course. Carlos and Emily are brilliant in their trainings.

創建者 Rowen

Oct 29, 2015

Teachers are really nice. Materials and the teaching are fantastic, I really learned a lot from this course. Thanks so much.

創建者 Sumit

Jul 08, 2016

Excellent course, gives good overview of all the different ML algorithms.

Case studies and assignments are really good and help a lot in understanding the concepts.

創建者 Fokhruz Z

Feb 28, 2016

Case Study Based Approach is VERY important in learning new technologies. Need some more practical Capstone Projects...

創建者 trevor s

Dec 06, 2015

Great format