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Opiniones y comentarios de aprendices correspondientes a Machine Learning: Regression por parte de Universidad de Washington

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5,472 calificaciones
1,016 reseña

Acerca del 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....

Principales reseñas

PD

16 de mar. de 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!

KM

4 de may. de 2020

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

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926 - 950 de 983 revisiones para Machine Learning: Regression

por 吴青

6 de dic. de 2017

actually good

por James H

12 de nov. de 2016

Great course

por Abhishek m

23 de ene. de 2021

nice course

por PHILIPPE R

26 de ene. de 2016

Nice course

por NIGAM P

1 de nov. de 2020

Great Job!

por Rohit K S

30 de sep. de 2020

Nice One!!

por Bruno G E

17 de abr. de 2016

Awesome!

por Sorin S

8 de may. de 2016

Great

por pavan k d

26 de nov. de 2021

good

por VIGNESHKUMAR R

23 de ago. de 2019

good

por Irfan S

17 de oct. de 2017

C

por Oliverio J S J

8 de jun. de 2018

This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the otThis course has interesting contents about regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that so much detail is necessary to understand what these algorithms do; more intuitive explanations are missing. On the other hand, as in the previous course, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.her hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.

por Terry S

18 de jul. de 2016

This course offers great background instruction on Machine Learning and I would give it 5 stars except for the following:

First, there doesn't seem to be any moderation of the session discussions except for help from other students. This was worth a -2 star penalty. This and the lack of any review of linear algebra and vectorized solutions, I think, is giving some students the impression that they should be coding loops in their functions to build and solve ML models.

Next, I am auditing the course, and this is the first course where I was not able to submit quizzes. Therefore, I can only guess at my solutions. This was worth a -1 star penalty.

UPDATE: not being able to submit quizzes is a "feature" of the new Coursera platform. I never did get an answer from the discussion forums, but I see the same problem in other Coursera courses I am taking.

However, I still think the course is worth taking, so I added back a star. This is the second ML course I have taken. The first was from Stanford ML course which was very specific to implementation in the Octave language. I got a lot more background information from this course, and I think it is well taught. Just wish there were more moderators that were actively watching the discussion list.

por Gabriele P

16 de abr. de 2019

The program is well structured, the lessons are interesting and the hands on nice. However, the instructor should really consider to update their material to python 3 + turicreate. Python 2 is reaching EOL in 2020 and should be avoided for teaching/training. I did most of my notebooks with python 3 and turicreate, it is really worth the effort to update the material. The tests are ok, but some looked somewhat buggy (as reported in the forum by many users) and could use a revision

por Rosen S

11 de jun. de 2021

Good topics and well enough explained, I really did learn a bit. But getting through the course is torture if you are using Sklearn (rather than using their tool TuriCreate). The Programming Assignments use different data sets (sometimes?) and are troublesome to download. From a purely UX viewpoint, the assignments are wordy/difficult to follow along with at some points (even when the content is not so difficult)

por Ahmed S

8 de dic. de 2019

The instructors have put a lot of effort into this course and I really appreciate that but unfortunately, I was hoping that the assignments were more interactive like in the deep learning specialization and the tool used is not required at all in any job I searched for also It's not required to use it. I learned a lot out of this course but please update the tools used in this course

por Thuc D X

18 de jun. de 2019

The program assignment's description was written badly and hard to follow

For example: in week 6's assignment, the description doesn't indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn't use.

por Erik P

8 de jun. de 2017

There are parts of the course which I got very very stuck on.. thankfully the forums have people's previous frustrations / questions on there. Reading these helped. Other than that, this course is the most comprehensive look at regression techniques I've taken yet, and I'm thankful that this course is provided.

por Sarah

15 de jul. de 2020

Assignments instructions are not very clear. Formulas used in assignments are structured differently then formulas in lectures. Too much emphasis on using turicreate. Not practical- companies do not ask for knowledge of turicreate. Companies ask for knowledge of scikit learn, pandas and numpy.

por Neelkanth S M

8 de abr. de 2019

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

por VINOJ J H

30 de jun. de 2016

Passing mark is 100%, it is tough for me and demotivating to persuade further. And the course becomes too extra factors and complexity on later classes, it made me to lose the interest on the algorithm and course.

I cannot complete it because of these two factors

por Debasish P

8 de feb. de 2020

The reading sections in module 4 had incorrect assumptions because of which I could not clear exams for months. Also the queries we posted in the forums are hardly responded. I just hope coursera takes support systems as actively as the contents

por Robert S

29 de nov. de 2016

Nice explanation and nice tasks but the course is designed for graphlab. If you want to use something else the tasks are often badly described or it is impossible to pass the

por Jaime S M O

8 de ene. de 2017

The material is excelente, But I would like you to promote a little more the community. Due to, sometime is difficult to advance when you don't understand a subject.

por Yuhuan Z

30 de ene. de 2020

Great indeed, but you have to rely on the Graphlab to realize those functions. You need to figure out whether you will use Graphlab in your future studies or work.