Chevron Left
返回到 Machine Learning: Regression

學生對 华盛顿大学 提供的 Machine Learning: Regression 的評價和反饋

4.8
5,480 個評分

課程概述

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python....

熱門審閱

PD

2016年3月16日

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!

KM

2020年5月4日

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the assignments...it’s just that turicreate library that caused some issues, however the course deserves a 5/5

篩選依據:

926 - Machine Learning: Regression 的 950 個評論(共 984 個)

創建者 Mehul P

2017年8月9日

Nicely explained.

創建者 Sandeep K S

2016年1月25日

excellent course

創建者 吴青

2017年12月6日

actually good

創建者 James H

2016年11月12日

Great course

創建者 Abhishek m

2021年1月23日

nice course

創建者 PHILIPPE R

2016年1月26日

Nice course

創建者 NIGAM P

2020年11月1日

Great Job!

創建者 Rohit K S

2020年9月30日

Nice One!!

創建者 Bruno G E

2016年4月17日

Awesome!

創建者 Sorin S

2016年5月8日

Great

創建者 pavan k d

2021年11月26日

good

創建者 VIGNESHKUMAR R

2019年8月23日

good

創建者 Irfan S

2017年10月17日

C

創建者 Oliverio J S J

2018年6月8日

This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the otThis course has interesting contents about regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that so much detail is necessary to understand what these algorithms do; more intuitive explanations are missing. On the other hand, as in the previous course, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.her hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.

創建者 Terry S

2016年7月18日

This course offers great background instruction on Machine Learning and I would give it 5 stars except for the following:

First, there doesn't seem to be any moderation of the session discussions except for help from other students. This was worth a -2 star penalty. This and the lack of any review of linear algebra and vectorized solutions, I think, is giving some students the impression that they should be coding loops in their functions to build and solve ML models.

Next, I am auditing the course, and this is the first course where I was not able to submit quizzes. Therefore, I can only guess at my solutions. This was worth a -1 star penalty.

UPDATE: not being able to submit quizzes is a "feature" of the new Coursera platform. I never did get an answer from the discussion forums, but I see the same problem in other Coursera courses I am taking.

However, I still think the course is worth taking, so I added back a star. This is the second ML course I have taken. The first was from Stanford ML course which was very specific to implementation in the Octave language. I got a lot more background information from this course, and I think it is well taught. Just wish there were more moderators that were actively watching the discussion list.

創建者 Rosen S

2021年6月11日

Good topics and well enough explained, I really did learn a bit. But getting through the course is torture if you are using Sklearn (rather than using their tool TuriCreate). The Programming Assignments use different data sets (sometimes?) and are troublesome to download. From a purely UX viewpoint, the assignments are wordy/difficult to follow along with at some points (even when the content is not so difficult)

創建者 Ahmed S

2019年12月8日

The instructors have put a lot of effort into this course and I really appreciate that but unfortunately, I was hoping that the assignments were more interactive like in the deep learning specialization and the tool used is not required at all in any job I searched for also It's not required to use it. I learned a lot out of this course but please update the tools used in this course

創建者 Thuc D X

2019年6月18日

The program assignment's description was written badly and hard to follow

For example: in week 6's assignment, the description doesn't indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn't use.

創建者 Erik P

2017年6月8日

There are parts of the course which I got very very stuck on.. thankfully the forums have people's previous frustrations / questions on there. Reading these helped. Other than that, this course is the most comprehensive look at regression techniques I've taken yet, and I'm thankful that this course is provided.

創建者 Sarah

2020年7月15日

Assignments instructions are not very clear. Formulas used in assignments are structured differently then formulas in lectures. Too much emphasis on using turicreate. Not practical- companies do not ask for knowledge of turicreate. Companies ask for knowledge of scikit learn, pandas and numpy.

創建者 Neelkanth S M

2019年4月8日

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

創建者 VINOJ J H

2016年6月30日

Passing mark is 100%, it is tough for me and demotivating to persuade further. And the course becomes too extra factors and complexity on later classes, it made me to lose the interest on the algorithm and course.

I cannot complete it because of these two factors

創建者 Debasish P

2020年2月8日

The reading sections in module 4 had incorrect assumptions because of which I could not clear exams for months. Also the queries we posted in the forums are hardly responded. I just hope coursera takes support systems as actively as the contents

創建者 Robert S

2016年11月29日

Nice explanation and nice tasks but the course is designed for graphlab. If you want to use something else the tasks are often badly described or it is impossible to pass the

創建者 Jaime S M O

2017年1月8日

The material is excelente, But I would like you to promote a little more the community. Due to, sometime is difficult to advance when you don't understand a subject.