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

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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


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!


4 de may. de 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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101 - 125 de 984 revisiones para Machine Learning: Regression

por Орлов А А

29 de jul. de 2019

I have finished this course and it was just great. Practical approach, great presentations, useful material. The course in fact require basic statistics and math, but the most fascinating thing about the course is how Carlos and Emily tend to explain really hard and cool material in very simple way. Great!

por YEH T P

11 de mar. de 2017

This is an amazing tour about regression and machine learning. You will learn basic linear regression first and dig into some practical problem include overfitting, feature selection, cross validation, this is a great course for people who interested in machine learning and have basic programming skill.

por Victor A M B

7 de jun. de 2020

Es un curso excelente, puede ser a veces un poco denso pero tiene conceptos demasiado importantes que cualquier persona que desee incursionar en el Machine Learning debería conocer. Muchas gracias a los tutores por ser tan claros en sus explicaciones, realmente impactan bastante en nuestro aprendizaje.

por Alex C

31 de mar. de 2021

excellent course, made lots of regression understandable instead of just being a black box. The course is fairly hard in that the coding is a bit more abstract than is covered in the explanations and i had to watch some things several times. But extremely well explained. Many thanks for a great course

por Charan S

22 de jul. de 2017

Amazing course which intuitive knowledge base. I personally liked the analysis part of every concept and algorithm via curves. This interpretation is very rare in most of the courses. Thanks for a such a beautiful course. And even the implementation via python graphLab was a good practise to learn.

por Dipanjan S

15 de may. de 2016

Excellent lectures with great explanations for the concepts as well as the mathematical equations and derivations. The assignments and concrete implementations also really helped reinforce the same concepts and to get a better idea of how it can be used to solve real world problem. Really amazing!

por nick

4 de nov. de 2016

It is really a lovely course! One point, among many others, that really amazed me is the geometric explanation of ridge and lasso! For me, I think the course would be better if the content can be more mathematically challenging but it is only a suggestion and the course is already quite perfect.

por Nil K P

31 de ago. de 2020

Course was very understanding and conceptually clear discussion.Assignments are also challenging but it will show you your patience level.Other courses show some algorithms but not implementation but trust me knowing implementation is very important as well as to know about the underline maths.

por Daniel R

7 de feb. de 2016

The detail level of the regression covered in this course is absolutely necessary if you want to achieve an outstanding level in the Machine Learning area.

The teacher is awesome and the capacity of making it simple for everyone is really from another planet! The best course I have taken so far!

por Sagara P

18 de jun. de 2016

Actually implemented Gradient Descent, Ridge Regression, Lasso etc.!!! No other course out their teaches the stuff contained in this one. Each algorithm is first run using a library. I used Pandas and SciKit. Then, we implement it from scratch! So the level of knowledge you gain is compounded.

por Kowndinya V

31 de mar. de 2018

This course gives deeper understanding of regression concepts. There were insights that are really helpful esp related to interpretation of coefficients. IMO, these insights are not obvious. Provides insights into different regression choices that are available along with their pros and cons.

por Wei F

6 de mar. de 2016

I like this course a lot. Frankly speaking, this is my first completed course on Coursera. The instructor is so good that I could easily follow everything in the class. Give lots of credits to the assignments. They're very easy to follow for me. I really enjoy working on them. Thanks a lot!!!

por Yaron K

14 de ago. de 2016

Prof. Emily Fox is definitely enthusiastic, and gives clear explanations. The assignments add to the understanding of the material. While Graphlab, which is idiosyncratic is still used, explanations are given how to use Sci-Kit learn. A technical course, not only ideas - put also algorithms.

por Anindya S

2 de ene. de 2016

Dr. Carlos Guestrin and Dr. Emily Fox are amazing. Needless to say, their way of teaching is absolutely brilliant and fun to learn, concepts which took me few days to learn now takes an hour or so, this is primarily due to their mastery on the subject matter and their lucid way of teaching.

por Mohit K

21 de abr. de 2018

I found this course more useful as compared to the first one. I really like this. One suggestion here, I would like you to incorporate is that you must have given small project work at the end of this course. This course is more technical and it would be helpful if we do some live project.

por Steve B

5 de mar. de 2017

This is one of the hardest courses I've ever taken. The theory part reminded me of Differential Equations, which got rid of about half of my electrical engineering class. Then they ask you to program it in Python!! There's a lot of great theory and deep explanations and hands-on coding.

por Manishkumar A J

23 de ago. de 2020

This course really help you to understand regression from basic to different version of regressions like ridge and lasso Regression. very well explained and assignment design such that it not only test your knowledge but also help to solve any doubt regrading concept just learn by you.

por Dohyoung C

11 de may. de 2019

Thank you for a good lecture.

The material was excellent and explanation was quite detailed and easy to understand.

Some of the programming was a little bit tricky, but I was able to pull through.

Thank you again for your efforts and I am looking forward to seeing you in the next course

por Tharuka K

9 de abr. de 2020

The top-down approach in this course is very interesting. They are presenting the theory very clearly. The course assignments are very well structured. I learned about regression algorithms and the theory behind it. Practiced to work with python and Turi Create and Sframes. Thank you

por Daniel V

10 de jun. de 2016

I do not this regression :D

Honestly, I can not thank Carlos and Emily enough given such a solid and casual understanding.

I am looking forward to every new Session and ofcourse the Capstone. But still a long way there, which is good news, more time with Carlos and Emily... yeah!

por Andre J

18 de mar. de 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

por Richard N B A

2 de feb. de 2016

Great course! Not simply a machine learning black box tutorial - like a few courses out there - but delves into the mathematics behind the algorithms (with several optional, more advanced excursions provided) and requires that we actually implement a few of the ideas ourselves.

por Freddie S

25 de jul. de 2016

Excellent combination of conceptual and practical quizzes. Providing the presentation slides is a great note-taking aid, as well as use of "ride-along" notebooks. The progressive use of the same dataset throughout the modules greatly aided focus on learning the algorithms.

por Nihal T

25 de sep. de 2017

Great course to get started in the Machine learning , it covers each and every concept of Regression . All the concepts are explained in so simple way that even a high school kid wont have any trouble understanding Machine learning . I would highly recommend taking this.

por Rohit G

29 de dic. de 2015

Absolutely loved the way Emily tackled the course content - the knowledge I gained regarding LASSO & RIdge was something even I wasn't expecting. Also the optional video helped a lot to understand the mathematics better as compared to a mechanical write-down of the steps