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Voltar para Machine Learning: Regression

Comentários e feedback de alunos de Machine Learning: Regression da instituição Universidade de Washington

5,175 classificações
972 avaliações

Sobre o curso

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....

Melhores avaliações


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!


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

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151 — 175 de 939 Avaliações para o Machine Learning: Regression

por Tyler B

Jan 01, 2016

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!


Feb 15, 2016

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.

por dharmesh s

Feb 08, 2020

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 .

por xun y

Feb 16, 2020

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.

por Manuel G

Jan 01, 2019

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.

por Bui T T (

Jan 17, 2016

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.

por Aarshay J

Mar 09, 2016

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.

por Tobi L

Jan 12, 2016

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!

por Rajesh P

Dec 30, 2015

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.

por shoubhik b

Jan 31, 2017

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 :(

por Michael H

Sep 02, 2016

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.

por Yu I

Aug 04, 2016

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.

por Yang X

Feb 14, 2016

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

por Ad T

Jul 17, 2017

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.

por Fernando F

Jan 12, 2016

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.

por Stephen M

Jan 23, 2018

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.

por Mohamed A M A E

Oct 17, 2017

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

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

por Alfred G

Jun 29, 2016

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.

por Virendra S S

Aug 09, 2018

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 .

por Syed T U S

Jun 28, 2018

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.


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

por Eftychios V

Jun 25, 2016

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!

por Rafael R d S

Nov 30, 2016

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

por Fede R

Jan 02, 2017

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.

por Aynalem M

Apr 26, 2020

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.