Volver a modelos de regresión

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3,298 calificaciones

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567 reseña

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....

KA

16 de dic. de 2017

Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.

DA

10 de mar. de 2019

This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!

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por Dror D

•5 de oct. de 2018

Good

por Ganapathi N K

•6 de may. de 2018

Good

por Yi-Yang L

•10 de may. de 2017

Good

por Larry G

•7 de feb. de 2017

Nice

por Sidra A 1

•13 de abr. de 2021

grt

por Amit K R

•21 de nov. de 2017

ok

por Ganesh P

•12 de mar. de 2018

V

por Priyanka V I

•26 de ago. de 2017

.

por Andrea F

•19 de abr. de 2017

B

por Benjamin G J

•5 de ene. de 2016

This is the best course of the bunch so far. These courses are really promising -- I've learned a lot from them and they probably have everything they could have at the price - but I'm leaving just one star off because I feel very strongly that some effort could really go a long way to making a better language map of these courses. A person leaves this course, and even more so the inference course, not being very clear one where their new capabilities lie in the spectrum, and without the strongest sense of how to experiment with linear models.

One case in point of the the huge strengths and a slight weakness of this course -- Professor Caffo mentions the wonderfully tantalizing fact that the application of linear models can get you most of the way to the top of a Kaggle competition. That feels true, I trust him, and it's really cool. But it would be SO. MUCH. COOLER. with an article showing a linear model attacking that kind of problem.

por John D M

•10 de abr. de 2019

Overall an excellent course, but there were some issues with the wrong function being specified in one quiz (Q3q6) and the wrong answer in another. Apparently it has been that way for years, according to the forum. The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that. However, the estimated time for completion of each week is ridiculously short compared to reality. Five hours? For me it was more like 20 hours, and more if I did all the Swirl exercises. Such low-balling on the time estimates is typical of the Data Science stream. The final project is given as 2 hours but it was closer to 15 for me. i wish Coursera would go back to the stream model where you could bump yourself to the next intake. That is much less stressful for busy working people like me.

por Amine A

•25 de ene. de 2021

I found this part of the course one of the hardest. But at the same time, probably the best course about linear regression I have ever seen. Is it difficult? Yes! Is it super exciting? Well... :) not necessarily. But I have come back to these course materials many times for a good reason. It's what you need to understand and use all the time. It is the absolute essential and necessary to know for any data scientist. While it might appear to be boring and basic compared to fancy deep learning models... trust me. It's not. It goes a long way in understanding what can be done with data. I am very grateful to the course instructors that they have spent their time and effort to make this course what it is. Please keep up the good work!

por Siying R

•10 de ago. de 2019

The lecture is pretty dry to me who had limited vocabulary in the field. It made me went out to find other easier lectures to help me understand. The lecture focus on explaining the basic concept of Regression Models and spend a big chunk of time to explain how the function works. I would prefer to have more time explaining what the numbers mean for the data. The questions in the quiz require us to understand the meaning of the data, so we know what function and number to apply. Maybe it is just me, finding it very challenging to see the connection between the lecture and the quiz.

por Romain F

•1 de abr. de 2017

Great course, although feeling as always a bit rushed on the last lectures. At least it makes you want to investigate more about the subject.

I find frustrating however not to have a proper instructor example of the final assignment, it is hard to review other participants work and realize what they / you have done wrong without actually knowing how best the assignment should have been fulfilled.

And as all courses in this specialization, there is not much interaction between participants, and not much effort by mentors to animate it

por Gianluca M

•20 de oct. de 2016

To me, this is by far the best course in the series. It deals with the scientific foundation of how to do data science: regression models, residuals, measures of the quality of the prediction, etc. The teacher is clearly a mathematician and has an academic style of presenting. He is very clear and chooses the subject in a clever way. One always understands what he or she is doing.

Highly recommended. It doesn't get five stars only because it covers only the basics; I would have really liked it to last twice as much!

por Shaochen H

•21 de ene. de 2018

Thanks for the great course! I think the following can be improved: 1) More depth, I find myself keep looking for additional materials from other sources, e.g. proof of different theories, the course only provides overview, but didn't go deep enough 2) Project: I find the optional quiz project more interesting, the final project is too simple, and didn't include things we learnt such as GLM etc. A more comprehensive final project with more aspects of courses knowledge will be much better to re-solidate learning

por Sandro G

•24 de jun. de 2017

This is the first time that I take a course about regression models. I I found it very useful and enteresting, may be for someone who already know this argument it could be less useful, because in some part it is lacking. I mean above all about some example that could be a little bit more complex than those presented in the videos and that more probably it could be more similar to real cases. In anycase, I would like to thank a lot the teachers and courser for this occasion to learn given to me and others !

por Pawel D

•18 de dic. de 2016

This course is much improved, when compared with Statistical Inference. The instructor have put much effort in making the lectures interesting and casual, at the same time not loosing the value of contents. I especially liked some subtle jokes - just a finishing human touch.

Some lectures from 4th week were not very well rehearser and shot hastily. Some quiz question were disproportionately difficult, but most of material is covered in course or course materials. Otherwise the course is very educational.

por Yusuf E

•15 de ago. de 2018

I am almost certain that regression models have more relavance in an academic setting than industry. But this doesn't affect really how I graded this course. I wish Brian skipped over the first week which entirely deals with regression to the mean. Weeks 2 and 3 were very good and detailed.

I am not sure if logistic classifier is mentioned in the next course but it would probably be best if this part would be included in the ML course. Other than that great course and very challenging quizzes.

por Amitabh M

•11 de abr. de 2020

This is the best course of the data science curriculum. The book is excellent, problem videos on YouTube teach how to answer the quizzes and pursue peer review project. The logistics regression part is weaker in the textbook as well as in lecture material. Prof. Brian Caffo is the best of the three teachers as he provides the most of the components needed either in the book or in his videos unlike others.

por Jamison C

•28 de ago. de 2018

Excellent course, though I recommend you supplement applied practice by using the principal instructor, Dr. Brian Caffo's book, to answer practice questions if you want to retain these content-packed lessons. Better yet, begin each week by looking at the quiz and printing it out. As you view the relevant content, answer the related questions (which are generally presented in order of delivery).

por Richard M A

•23 de dic. de 2016

This was better than the statistical inference course, but Brian still puts too much emphasis on the precision of his language (as if he's teaching to other mathematicians) which makes it difficult to understand. I would like to see a bit more dumbed down explanation of the mathematics in the examples (similar to Sal at Khan Academy). If that happened, this would definitely be a 5 star course.

por Sandesh

•25 de jun. de 2019

For the content covered, I think the course does a good job exposing students to fundamental concepts while also highlighting how much more there is to research in order to gain a solid understanding of this subject matter. The course offers a good foundation, and I hope they come out with a more advanced version of this course for more guided exposure.

por Christopher B

•28 de feb. de 2017

This course was an improvement in teaching modality from the statistical inference course, with more polished content, but the link between the lectures and the actual exercises was still a bit strained. Overall, it felt like there was a bit of a disconnect between the swirl exercises and the lectures, and this led to a lot of self-teaching.

por Paul R

•13 de mar. de 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

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