Вернуться к Machine Learning: Regression

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Оценки: 4,530

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Рецензии: 846

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....

Mar 17, 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!

Jan 27, 2016

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

Фильтр по:

автор: Charlotte B

•Jul 24, 2019

I definitely learned a lot in this class about different techniques and ways to use regression in machine learning. I also feel like I learned a lot about how to program in Python.

автор: Liang-Yao W

•Jun 26, 2017

I like the step-by-step introduction that familiarize one with the important concepts. I also like the nice explanation and visualization of some relevant mathematics. Recommended.

автор: Oshan M

•Jun 23, 2017

thorough explanation. they cover most of the topics. lessons on ridge and lasso regression are great. would recommend for anyone looking to get into data science/ machine learning.

автор: Holger P

•Sep 30, 2016

Great course covering Regression Machine Learning. Gives a great introduction to this topic. Teaching the methods using a case study yields for great illustrations of the concepts.

автор: Andrea C

•Aug 16, 2016

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

•Mar 09, 2016

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

•Aug 04, 2019

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

•Feb 07, 2016

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

•Mar 19, 2018

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

•Mar 02, 2018

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

•Aug 27, 2017

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

•Mar 30, 2016

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

•Dec 11, 2017

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

•May 06, 2017

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

•Apr 03, 2017

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

•Dec 28, 2015

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

•Sep 17, 2019

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

•May 19, 2018

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

•May 13, 2016

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

•Jan 14, 2016

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

•Jan 06, 2016

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

•Aug 15, 2016

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

•May 16, 2016

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

•Jan 25, 2016

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

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

автор: Thomas K A W

•Jan 08, 2018

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!