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Voltar para Machine Learning: Regression

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

4.8
estrelas
5,219 classificações
977 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

KM
4 de Mai 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

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!

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901 — 925 de 945 Avaliações para o Machine Learning: Regression

por Irfan S

17 de Out 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

15 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 Ahmed S

8 de Dez 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 Fev 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 Jan 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 Jan 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.

por tim h

6 de Jul de 2016

Rather elementary and slow-moving for my taste. But the material is competently presented and covers the material it is advertised to cover.

por Marco P

17 de Jan de 2016

Missing in-lesson quiz, with all the homeworks being at the end of the week: this make following the pace quite tough

por Cameron B

20 de Set de 2016

Good course content but it can be very difficult to get help if you are stuck on something.

por Mahbub A

7 de Fev de 2016

Course materials are well organized. It could be improved by adding more description.

por Pratyush K D

17 de Jul de 2020

Please show examples of codes in lectures just like the previous course

por Shreyo M

24 de Dez de 2016

The course lacks deep theoretical understanding of the concepts.

por Akash B

11 de Fev de 2019

Course should contain a project related to real life.

por Saiprasad B

7 de Jan de 2017

very interesting environment to learn the subject.

por Konstantin B

18 de Nov de 2017

Too much math...

por Deleted A

23 de Nov de 2016

I'm a statistician, not a programmer. There is so much detail and explanation about the statistics and concepts behind how it works, but there is hardly ever an actual lesson on the code used or needed to implement the algorithms. When trying to fumble my way through the code, I found, on several occasions, the code in the self-directed lessons to be incomplete (I'm referring to pieces of code that were obviously meant to be there, but were missing), causing hours and hours of anguish and turmoil. I feel like there should be a lot more time spent on the actual coding and learning how to implement it within the code (similar to the 1st course), rather than spending an exuberant amount of time going through derivations and no time on actual coding and how to implement it within the programming language.

If you are a software designer/engineer or programmer, then you should be fine as long as you pay attention to the very long lessons and derivations and can fix the broken code that you are given. There are other mistakes within the quizzes as well, which make them near impossible to pass. For example, it is unclear which model you need to use to calculate in order to get the correct square foot. On other occasions, the question actually specifies to use the model from (3), whereas it actually wants you to use the model from (4) instead to get the correct answer. This course needs to have better quality checks to ensure needlessly lost time is minimized.

por Peter H

28 de Ago de 2016

There are two frustrations with this iteration of the series. One: the quiz questions are often opaquely worded. Instead of being tested on the material just learned, it seemed like the objective was to learn to decode test questions.

2 and the most glaring omission, was that when students are asked to provide functions, only some are provided with a follow up test to ensure the function is working properly. If the output is syntactically correct but provides incorrect output then you're moving forward blindly after that. Then add the quiz questions from problem One above, and you're just wasting your time after that and building up frustration. Validating your code as you move along seems like a pretty rudimentary process to impart to students and when the teachers don't practice it themselves, there are bound to be problems.

I like the intent of the course, and considering my outsider background to computer science, the mathematics etc, I did learn a fair bit. Not enough to justify the increasing frustration I was feeling toward the end of this course. I have no intention of taking any more at this point, not from these authors.