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Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4,948개의 평가
930개의 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


May 05, 2020

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


Mar 17, 2016

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!

필터링 기준:

Machine Learning: Regression의 899개 리뷰 중 101~125

교육 기관: Daniel R

Feb 07, 2016

The detail level of the regression covered in this course is absolutely necessary if you want to achieve an outstanding level in the Machine Learning area.

The teacher is awesome and the capacity of making it simple for everyone is really from another planet! The best course I have taken so far!

교육 기관: Sagara P

Jun 19, 2016

Actually implemented Gradient Descent, Ridge Regression, Lasso etc.!!! No other course out their teaches the stuff contained in this one. Each algorithm is first run using a library. I used Pandas and SciKit. Then, we implement it from scratch! So the level of knowledge you gain is compounded.

교육 기관: Kowndinya V

Apr 01, 2018

This course gives deeper understanding of regression concepts. There were insights that are really helpful esp related to interpretation of coefficients. IMO, these insights are not obvious. Provides insights into different regression choices that are available along with their pros and cons.

교육 기관: Wei F

Mar 07, 2016

I like this course a lot. Frankly speaking, this is my first completed course on Coursera. The instructor is so good that I could easily follow everything in the class. Give lots of credits to the assignments. They're very easy to follow for me. I really enjoy working on them. Thanks a lot!!!

교육 기관: Yaron K

Aug 14, 2016

Prof. Emily Fox is definitely enthusiastic, and gives clear explanations. The assignments add to the understanding of the material. While Graphlab, which is idiosyncratic is still used, explanations are given how to use Sci-Kit learn. A technical course, not only ideas - put also algorithms.

교육 기관: Anindya S

Jan 02, 2016

Dr. Carlos Guestrin and Dr. Emily Fox are amazing. Needless to say, their way of teaching is absolutely brilliant and fun to learn, concepts which took me few days to learn now takes an hour or so, this is primarily due to their mastery on the subject matter and their lucid way of teaching.

교육 기관: Mohit K

Apr 21, 2018

I found this course more useful as compared to the first one. I really like this. One suggestion here, I would like you to incorporate is that you must have given small project work at the end of this course. This course is more technical and it would be helpful if we do some live project.

교육 기관: Steve B

Mar 05, 2017

This is one of the hardest courses I've ever taken. The theory part reminded me of Differential Equations, which got rid of about half of my electrical engineering class. Then they ask you to program it in Python!! There's a lot of great theory and deep explanations and hands-on coding.

교육 기관: Dohyoung C

May 11, 2019

Thank you for a good lecture.

The material was excellent and explanation was quite detailed and easy to understand.

Some of the programming was a little bit tricky, but I was able to pull through.

Thank you again for your efforts and I am looking forward to seeing you in the next course

교육 기관: Tharuka K

Apr 09, 2020

The top-down approach in this course is very interesting. They are presenting the theory very clearly. The course assignments are very well structured. I learned about regression algorithms and the theory behind it. Practiced to work with python and Turi Create and Sframes. Thank you

교육 기관: Daniel V

Jun 10, 2016

I do not this regression :D

Honestly, I can not thank Carlos and Emily enough given such a solid and casual understanding.

I am looking forward to every new Session and ofcourse the Capstone. But still a long way there, which is good news, more time with Carlos and Emily... yeah!

교육 기관: Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

교육 기관: Richard N B A

Feb 02, 2016

Great course! Not simply a machine learning black box tutorial - like a few courses out there - but delves into the mathematics behind the algorithms (with several optional, more advanced excursions provided) and requires that we actually implement a few of the ideas ourselves.

교육 기관: Freddie S

Jul 25, 2016

Excellent combination of conceptual and practical quizzes. Providing the presentation slides is a great note-taking aid, as well as use of "ride-along" notebooks. The progressive use of the same dataset throughout the modules greatly aided focus on learning the algorithms.

교육 기관: Nihal T

Sep 25, 2017

Great course to get started in the Machine learning , it covers each and every concept of Regression . All the concepts are explained in so simple way that even a high school kid wont have any trouble understanding Machine learning . I would highly recommend taking this.

교육 기관: Rohit G

Dec 29, 2015

Absolutely loved the way Emily tackled the course content - the knowledge I gained regarding LASSO & RIdge was something even I wasn't expecting. Also the optional video helped a lot to understand the mathematics better as compared to a mechanical write-down of the steps

교육 기관: Suresh A

Apr 19, 2016

Fantastic course.

Lot of courses I have taken do not give the mathematical formulation. This course provides a detailed understanding of the math behind ML.

Also in the programming exercises one implements the algorithms from scratch and also use the existing libraries.

교육 기관: Mohamed A

May 01, 2016

Great course! all materials are well structured and introduce each concept concisely. I enjoyed all programming assignments. Take this course if you would like to know more about regression rather than simply finding the perfect hyper-plane that approximate your data.

교육 기관: Victor C

May 28, 2017

Emily Fox is exceptional. It's a smooth airplane ride through often turbulent paths. That's harder to do than it might seem as most teachers get mired in details that confuse and/or distract the student. I would think that any course she teaches is worth taking.

교육 기관: Abhinav U

Jan 11, 2016

Great course, very detailed and hands on, also including appropriate amount of mathematical rigour to help you understand what is going on under the hood. Highly recommended. I specially liked the modules on Ridge regression and Lasso regression, really well done.

교육 기관: Dan L

Dec 28, 2015

I found this an excellent introduction to the topic with a good mix of well-presented material and practical application using the IPython notebooks. I would love to have the course finish with a project where we apply the learned methods to a different data set.

교육 기관: 张明

Dec 04, 2015

Very responsible teachers and practical classes content.You can not only learning the ML theory from scratch,but also learn to implement the algorithm using python by yourself.This is the best ML course I ever seen.

Thanks for the teachers' hard-work.You are great!

교육 기관: Muhammad U C

Feb 12, 2016

Excellent. This is an ideal course in order to understand various aspects of regression techniques. Explanation using hands-on exercises helps me learn this course very effectively. I must appreciate the efforts of both Instructors (Prof. Emily & Prof. Carlos).

교육 기관: Amal G

Sep 10, 2016

I felt that the course was detailed and contained significant in-depth study about regression techniques. The assignments were well designed, starting from a single step and eventually enabling the candidate to be able to write the complete methods on his own.

교육 기관: Lech G

Jan 06, 2016

This is probably the best Coursera course I have completed so far (and I am kind of Coursera junkie). very well structured, the right amount of math and driven by the experiments on the real data.

Looking forward to Classification course and others in series.