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Вернуться к Machine Learning: Regression

Отзывы учащихся о курсе Machine Learning: Regression от партнера Вашингтонский университет

4.8
звезд
Оценки: 5,211
Рецензии: 976

О курсе

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....

Лучшие рецензии

KM
4 мая 2020 г.

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 мар. 2016 г.

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!

Фильтр по:

201–225 из 943 отзывов о курсе Machine Learning: Regression

автор: Andrea C

16 авг. 2016 г.

This course is damn well structured. Course material is great and programming assignments are interesting and helps you to really understand how to implement regression algorithms.

автор: George K

9 мар. 2016 г.

The professors help understand the concepts from ground up. Seriously recommended course if you want to know how Regression works and all about ridge, lasso and kernel regression.

автор: Yabin W

4 авг. 2019 г.

The course goes into great details to clarify difficult concepts. Besides, the assignments are well designed so that students can grasp the topic step by step through practicing.

автор: Lennart B

7 февр. 2016 г.

Thorough introduction to regression, the assignments are demanding, and the teachers very engaging. It would be nice if a wider range of applications and examples were presented.

автор: Joseph F

19 мар. 2018 г.

Very good course with nice slides and clear interpret, and the assignment with ipython is really well designed because it already give you the illustration of each step. Thanks!

автор: Ed S

2 мар. 2018 г.

You will get a good grasp of Linear Regression, Ridge Regression, Lasso and potential use for feature selection, gradient descent, coordinate descent, numpy and graphlab create

автор: Salim L

27 авг. 2017 г.

Goes well beyond the statistics that I learned in engineering! Key concepts in regression such Ridge, Lasso and KNN. Use Python to build all your algorithms from the ground up.

автор: Omar N T

30 мар. 2016 г.

it gave more details than my class room. it also adopts a practical approach to learn. it is a great course in regression especially for practitioners.

Thanks Carlos and Emily :)

автор: Dipankar N

11 дек. 2017 г.

Great course on Regression. This will help build basic for upcoming modules. Emily teaches the concepts in a simple way. I liked the structure and coverage of Regression topic.

автор: Nadya O

6 мая 2017 г.

Great material, this was tougher than the previous course. It is challenging and more exercises to practice which help to a better understanding of the concepts. Great mentors!

автор: Rahul J

2 апр. 2017 г.

An extremely well designed course, I am an instructional designer myself, so adding weight to the words. Would have appreciated a few more assignments for the last week though.

автор: Chengcheng L

27 дек. 2015 г.

I feel I understand regression models better than before. But I still need to read more books on the same topic to actually convert what I learned here to long term memory :)

автор: Lavaneesh S

17 сент. 2019 г.

Fantastic Course, allowed me to gain insights to regression. Both the instructors like always have been excellent. Shout out to coursera for allowing me to take this course!

автор: 陈哲鸿

19 мая 2018 г.

It's a really nice course.What i've learned in this course: how to implement a regression model through my own hands, assessing performance, feature selection...and so on.

автор: clara c

13 мая 2016 г.

This course is very well organized and all the information is relevant. Everything is explained in great detail. The exercises really make you feel that you are learning.

автор: Do H L

13 янв. 2016 г.

All the courses in this specializations are very well-made and rigorous. I think all MOOCs, especially techinical ones, should be as well-designed as this or even more.

автор: Fahim K

6 янв. 2016 г.

The course is really helpful. It has started with simple Regression model and gradually build the different advance regression model. Thanks for this wonderful course.

автор: Aditya K

15 авг. 2016 г.

rigorously explained some of the most important algorithms in regression world, also the pros and cons of using certain algorithm for certain conditions. totally worth

автор: Sahil D

15 мая 2016 г.

Good overall theoretical and practical explanation of the material, I was also able to use scikit learn and pandas without any difficulties instead of graphlab create.

автор: isanco

25 янв. 2016 г.

Great class (really liked the graphical interpretations of Lasso and Ridge optimizations).

Perhaps some quizzes (and especially assignements) could be more challenging?

автор: Iñaki D R

11 июля 2020 г.

Great course, excelent professors & simple yet accurate explanations, always guiding you through the course and through practical implementation of acquired knowledge

автор: Thomas K A W

8 янв. 2018 г.

Great course! I love the instructors and the thoroughly designed structure of their course. The effort they put into this course certainly shines through every video!

автор: Jessie J S

12 мая 2018 г.

I love this course! It explains more about Regression itself and not just discussing on how to use libraries for it! Very intuitive and informative at the same time!

автор: Kapil K

14 февр. 2017 г.

its a great course. little bit disappointed from the decision of not continuing Recommended systems and capstone project. PLEASEEEEEE roll out course 5 and 6 as well

автор: Saheed S

19 сент. 2017 г.

Nice course. I started with this specialization as a beginner. I was very intuitive and great course I would recommend to others people interested in data science.