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

4.8
5,462 個評分
1,014 條評論

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....

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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

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201 - Machine Learning: Regression 的 225 個評論(共 982 個)

創建者 Charlotte B

2019年7月24日

I definitely learned a lot in this class about different techniques and ways to use regression in machine learning. I also feel like I learned a lot about how to program in Python.

創建者 Liang-Yao W

2017年6月26日

I like the step-by-step introduction that familiarize one with the important concepts. I also like the nice explanation and visualization of some relevant mathematics. Recommended.

創建者 Oshan

2017年6月22日

thorough explanation. they cover most of the topics. lessons on ridge and lasso regression are great. would recommend for anyone looking to get into data science/ machine learning.

創建者 Holger P

2016年9月30日

Great course covering Regression Machine Learning. Gives a great introduction to this topic. Teaching the methods using a case study yields for great illustrations of the concepts.

創建者 Andrea C

2016年8月16日

This course is damn well structured. Course material is great and programming assignments are interesting and helps you to really understand how to implement regression algorithms.

創建者 George K

2016年3月9日

The professors help understand the concepts from ground up. Seriously recommended course if you want to know how Regression works and all about ridge, lasso and kernel regression.

創建者 Yabin W

2019年8月4日

The course goes into great details to clarify difficult concepts. Besides, the assignments are well designed so that students can grasp the topic step by step through practicing.

創建者 Lennart B

2016年2月7日

Thorough introduction to regression, the assignments are demanding, and the teachers very engaging. It would be nice if a wider range of applications and examples were presented.

創建者 Joseph F

2018年3月19日

Very good course with nice slides and clear interpret, and the assignment with ipython is really well designed because it already give you the illustration of each step. Thanks!

創建者 Ed S

2018年3月2日

You will get a good grasp of Linear Regression, Ridge Regression, Lasso and potential use for feature selection, gradient descent, coordinate descent, numpy and graphlab create

創建者 Salim L

2017年8月27日

Goes well beyond the statistics that I learned in engineering! Key concepts in regression such Ridge, Lasso and KNN. Use Python to build all your algorithms from the ground up.

創建者 Omar N T

2016年3月30日

it gave more details than my class room. it also adopts a practical approach to learn. it is a great course in regression especially for practitioners.

Thanks Carlos and Emily :)

創建者 Dipankar N

2017年12月11日

Great course on Regression. This will help build basic for upcoming modules. Emily teaches the concepts in a simple way. I liked the structure and coverage of Regression topic.

創建者 Nadya O

2017年5月6日

Great material, this was tougher than the previous course. It is challenging and more exercises to practice which help to a better understanding of the concepts. Great mentors!

創建者 Rahul J

2017年4月2日

An extremely well designed course, I am an instructional designer myself, so adding weight to the words. Would have appreciated a few more assignments for the last week though.

創建者 Chengcheng L

2015年12月27日

I feel I understand regression models better than before. But I still need to read more books on the same topic to actually convert what I learned here to long term memory :)

創建者 Lavaneesh S

2019年9月17日

Fantastic Course, allowed me to gain insights to regression. Both the instructors like always have been excellent. Shout out to coursera for allowing me to take this course!

創建者 陈哲鸿

2018年5月19日

It's a really nice course.What i've learned in this course: how to implement a regression model through my own hands, assessing performance, feature selection...and so on.

創建者 clara c

2016年5月13日

This course is very well organized and all the information is relevant. Everything is explained in great detail. The exercises really make you feel that you are learning.

創建者 Huynh L D

2016年1月13日

All the courses in this specializations are very well-made and rigorous. I think all MOOCs, especially techinical ones, should be as well-designed as this or even more.

創建者 Fahim K

2016年1月6日

The course is really helpful. It has started with simple Regression model and gradually build the different advance regression model. Thanks for this wonderful course.

創建者 Aditya K

2016年8月15日

rigorously explained some of the most important algorithms in regression world, also the pros and cons of using certain algorithm for certain conditions. totally worth

創建者 Sahil D

2016年5月15日

Good overall theoretical and practical explanation of the material, I was also able to use scikit learn and pandas without any difficulties instead of graphlab create.

創建者 isanco

2016年1月25日

Great class (really liked the graphical interpretations of Lasso and Ridge optimizations).

Perhaps some quizzes (and especially assignements) could be more challenging?

創建者 Iñaki D R

2020年7月11日

Great course, excelent professors & simple yet accurate explanations, always guiding you through the course and through practical implementation of acquired knowledge