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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

5,354개의 평가
999개의 리뷰

강좌 소개

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....

최상위 리뷰

2016년 3월 16일

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!

2020년 5월 4일

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

필터링 기준:

Machine Learning: Regression의 966개 리뷰 중 251~275

교육 기관: vishnu v

2016년 1월 2일

Great course on regression. Covers almost all aspects on how to build a regression model from scratch, also covers few advanced topics aswell.

교육 기관: Jane T

2017년 6월 30일

Difficult material, but the style of the lectures and assignments managed to keep it fun and interesting, all the way to the end. Amazing job

교육 기관: 戴维

2016년 3월 6일

It is an excellent course, which can not only equip you with tools but also allow you to know the underlying reason. And it is interesting.

교육 기관: Leandro L R

2018년 5월 12일

This course is very good. It went above my expectations. The instructors are great and I learned a lot of Python here. I really recommend.

교육 기관: Nicolas P L

2020년 7월 13일

Great Course, it focused both in the theory and practical approaches in a challenging way such that you could learn better the concepts.

교육 기관: Yashaswi P

2018년 9월 13일

The only hindrance I had is with understanding the problem statements in assignments. It would be better to use a more unambiguous text.

교육 기관: Md F A

2019년 11월 11일

This is probably most in-depth Regression learning with python code, I have ever had. I liked the detail adventures of quizz questions.

교육 기관: Hemant V G

2016년 3월 14일

Course has covered regression in sufficient details and gave practical aspect of it. Thanks to Emily for very good content and teaching

교육 기관: Sathiraju E

2018년 10월 31일

It was great to take this course. Thanks to Carlos and Emily for their efforts. It's been a useful course and certainly worth my time.

교육 기관: Bharath K M

2020년 7월 19일

Very helpful in building good basics about regression and ML. Programming questions are very useful for practice and nicely prepared.

교육 기관: Harley J

2017년 7월 18일

Very solid course for understanding machine learning principles, including developing methodical approaches to solving data problems.

교육 기관: Joanna L

2016년 3월 14일

Excellent, step-by-step introduction to regression. The instructor takes her time to make sure every step is explained with details.

교육 기관: Xavi R

2020년 5월 14일

I loved this course. This is an excellent course for getting started in Machine Learning and I hope to complete the specialization.

교육 기관: Aayush A

2018년 7월 12일

This course is very good.I learnt a lot from it about regression.very recommended for all trying to get expert in machine learning.

교육 기관: Maria Z

2017년 12월 27일

Much more difficult than the first course. It would be challenfing for those who don't have programming skills and math background.

교육 기관: Michele P

2017년 8월 23일

Very nice explanation of ridge and lasso regression. Assignments are easier than in Classification. I highly recommend this course!

교육 기관: Ali A

2016년 3월 5일

All what I can say is if there is ten stars I would have given them to this course. It is just amazing and very very very helpful.

교육 기관: Pranas B

2016년 3월 18일

Amazing course with good balance of visual material, practice, and optional math. Thanks Emily and Carlos, you are great teachers!

교육 기관: Jacob M L

2016년 3월 1일

Well presented, practical, and hands-on. By far the best Data Science / Machine Learning series I have taken thus far on Coursera.

교육 기관: Surendar R

2018년 12월 23일

In Depth coverage of lot of concepts, fully enjoyed it! Recommended to anyone wanting to explore in depth concepts of regression.

교육 기관: Abe E

2017년 4월 28일

Excellent. I used some of the videos to prepare and brush up for job interviews. Super helpful to play back at double speed ;-)

교육 기관: Wafic E

2016년 11월 6일

An amazing course. You can sense the effort put into the presentations and assignment work. Loving the specialization thus far.

교육 기관: Sergio D H

2016년 2월 6일

One of the best MOOCs I've ever tried. Great course materials and incredibly talented instructors. I can't recommend it enough.

교육 기관: Luciano S

2017년 8월 7일

I learned a lot of new concepts in this course. It is important to dive deeper than just understing how to use a set of tools.

교육 기관: Rama K R N R G

2017년 8월 19일

I really liked the progression of the topics and coverage. Good presentation with good amount of details/depth in each topic.