Chevron Left
Machine Learning: Regression(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Regression 학습자 리뷰 및 피드백

4.8
별점
5,225개의 평가
978개의 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

PD
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!

KM
2020년 5월 4일

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

필터링 기준:

Machine Learning: Regression의 945개 리뷰 중 151~175

교육 기관: Tyler B

2015년 12월 31일

Excellent course on Regression! From the basics up to some pretty complicated stuff, Emily Fox did a great job explaining the concepts and the programming assignments were challenging without being overwhelming. Well done!

교육 기관: SMRUTI R D

2016년 2월 15일

A very detailed course on regression with real data examples and which exposes the student to actual coding of different functions, rather than using already available functions. I got a very satisfying learning experience.

교육 기관: dharmesh s

2020년 2월 7일

this course is excellent for me .it gives me a deeper understanding of algorithms and concepts. this course also gives me direction to my career . thanks coursera and university of washington for providing such platform .

교육 기관: xun y

2020년 2월 16일

Very informative course. The best part is the visualization of ridge regression and lasso regression optimization. It would be great if the professor can add one final project to walk through the entire modeling process.

교육 기관: Manuel G

2019년 1월 1일

Amazing course! Thoroughly enjoyed it, and really appreciated the level of detail in some of the theoretical concepts. Yet it also stayed within what's practically useful and had a good amount of hands-on implementation.

교육 기관: Bui T T (

2016년 1월 16일

What a great course about machine learning I've been taken so far! One of the best thing (I like) for this course is that I have deep understanding and I am able to implement the machine learning algorithms by myself.

교육 기관: Aarshay J

2016년 3월 9일

A very good starting to the journey to Machine Learning. Just one disappointment, I was expecting the classification and clustering courses to start together but the specialization has been delayed by a long time now.

교육 기관: Tobi L

2016년 1월 11일

There was way more interesting mathematics to linear regression than I ever imagined. I thought this was going to be a boring review of linear algebra and quadratic polynomials. I have never been so happy to be wrong!

교육 기관: Rajesh P

2015년 12월 30일

I really got a lot of the course. The material is explained very well. The programming assignments helped further the understanding. The recap video that summarizes the entire module in 10-15 min is also very good.

교육 기관: shoubhik b

2017년 1월 31일

Very thorough. If you are beginner this course will give you the tools to do further study by yourself. I still go back to the lectures to refresh a few concept. Really sad that course 5 and 6 won't be released :(

교육 기관: Michael H

2016년 9월 2일

Fantastic course. Perfect balance of practice and theory. I have tried learning regression a number of times now and after doing this course I feel like I finally have a good grasp on it. Absolutely no complaints.

교육 기관: Yu I

2016년 8월 4일

This course was super exciting! The explanation was very intuitive, using nice visualizations. The programming assignments was really practical. It would be great for machine learning newbees to learn regression.

교육 기관: Yang X

2016년 2월 14일

Love this course! Love the flexibility of the course but if rigor is what you want, they offer mathematical rigor in optional lectures as well. Great lectures and well-designed assignments. Highly recommended

교육 기관: Ad T

2017년 7월 17일

Great course with just the right level of detail. Had lots of fun implementing the algorithms in Python based on the instructions and all the examples helped really understand what is happening under the hood.

교육 기관: Fernando F

2016년 1월 12일

I think this course has been very interesting. Regression is too wide for covering entirely in a course like this but it has provided me with the basic knowledge and fundamentals to keep working in the matter.

교육 기관: Stephen M

2018년 1월 23일

I enjoyed the math and the Python exercises, which were interesting and challenging. The functions and algorithms used in the notebooks would be a good starting point for a set of Python regression classes.

교육 기관: Mohamed A M A E

2017년 10월 17일

it is a good contant and i learn more information such as

Simple linear regression, Multiple regressionAssessing , performanceRidge , regressionFeature selection & LassoNearest , neighbor & kernel regression

교육 기관: Alfred G

2016년 6월 29일

I strongly recommended you guys to walk through this course. It worth it! And the programming assignment is awesome. I also recommended that you can try to use sklearn + pandas + numpy to rebuild your code.

교육 기관: Virendra S S

2018년 8월 9일

awesome course.Regression concepts are deeply covered .Be careful doing assignments .assignments are long but they are from scratch you will get to know to how machine learning algorithm actually works .

교육 기관: Syed T U S

2018년 6월 27일

This course is amazing and cover a wide range of topics. It broadband my knowledge in the core area of machine learning. The course content and teaching style is tremendous. Thank you Coursera and UoW.

교육 기관: KYRIAKOS M

2020년 5월 5일

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

교육 기관: Eftychios V

2016년 6월 25일

An in-depth overview of the regression techniques and models. I think it went as deep into the concepts as I wanted it to go. Being a developer I found it quite understandable, and useful.

Keep it up!

교육 기관: Rafael R d S

2016년 11월 30일

Excelent course, I highly recommend for those who are willing to learn machine learning from the basis, this module (linear regression) covered very important parts that I used to struggle to learn

교육 기관: Fede R

2017년 1월 2일

This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.

교육 기관: Aynalem M

2020년 4월 26일

Very informative, practical course with excellent instructors, I would recommend this course to anyone doing basic machine learning. The only issue I see is that the course can be offered in R.