Вернуться к Machine Learning: Regression

4.8

Оценки: 4,471

•

Рецензии: 839

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: Pantelis H

•Apr 07, 2016

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

автор: leonardo d

•Oct 28, 2018

Excellent course, the professors made it very easy to learn quite powerful technics like gradient descend and coordinate descend. I always saw them like black-boxes, but now, thanks to this course I not only understand how they really work, but I learned how to apply them to real data. This course was simply awesome.

автор: Konduri V

•Dec 25, 2018

I really enjoyed learning through out this course. I did little bit struggle with Python but now I am a bot more confident to take on advanced programming in Python.

Thank you very much for offering course.

автор: Jafed E

•Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

автор: Hiral P

•Oct 09, 2018

I loved this course because of the detail understanding of the concepts. I was looking for a course which provide detail understanding of algorithms, and here I am. I am giving four stars for what has been given in detail, not five because I something is left ;) interpretation..

автор: Ernie M

•Sep 25, 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

автор: Kunal T

•Dec 19, 2018

Extremely well designed course.

автор: Xue

•Dec 08, 2018

Very well-organized and clear. Learned a lot about regression.

автор: Phil O

•Dec 10, 2018

4.9 Stars really but had to round. Really enjoyable course and extremely well presented. As a working statistician/analyst this stuff hits on a lot of the import underlying logic that needs to be in your head when looking at real world projects. The 0.1 star drop is because some of the language in the questions can be confusing, an easy fix.

автор: Surendar R

•Dec 23, 2018

In Depth coverage of lot of concepts, fully enjoyed it! Recommended to anyone wanting to explore in depth concepts of regression.

автор: Refael J

•Dec 23, 2018

Very good course. The lecturer is very good and the information is very comprehensive.

автор: Ilias A

•Dec 30, 2018

Wow, just wow ! This course had a great scope, digging in on the concepts / methodologies that are crucial for regression, while at the same time discussing more general and always-present concepts of a machine learning task. A learning powerhouse ! I think i must pass it a second time, to really get into the details.

автор: Christopher M

•Jan 26, 2019

Great course. You get to write the algorithms for OLS regressions, ridge regression, lasso regression, and for k-nearest neighbor models. The instruction even includes some optional graduate-level videos on with more detailed explanations of how more advanced algorithms for solving the regressions may be developed (eg, subgradients for lasso regression).

автор: Ganji R

•Nov 08, 2018

E

автор: Aftab N

•Nov 22, 2018

Amaizing one.. You Rocks

автор: Pavan B

•Jan 21, 2019

Very good assignments.

автор: Yamin A

•Feb 10, 2019

Excellent course that is the second in this specialization. It goes beyond the Foundations course and delves further into utilizing machine learning with regression based methods. The course also uses Python. There is some requirement that you should have some degree of familiarity with programming, although you can pick up some skills in coding in Python even if you are not familiar with it (- I wasn't familiar with Python much, although I am familiar with other languages).

Overall, highly recommended.

автор: Zhongkai M

•Feb 12, 2019

It provided practical details the are not described to much in others' courses.

автор: Ayush K

•Mar 08, 2019

Great in-depth coverage

автор: Akash G

•Mar 09, 2019

regression best now

автор: DHARMVIR K

•Aug 08, 2018

It was great learning

автор: Virendra S S

•Aug 09, 2018

awesome course.Regression concepts are deeply covered .Be careful doing assignments .assignments are long but they are from scratch you will get to know to how machine learning algorithm actually works .

автор: Anantha P

•Aug 06, 2018

Great course on Regression. This course explains the basic regression algorithms and the math behind these algorithms in a way that is easily understandable. Apart from the explanation, the assignments are also awesome, where you get to try out all the algorithms in the machine learning libraries as well as implement them your own.

автор: Charlie Q

•Aug 11, 2018

Very clear and detailed presentation of concepts and techniques of the traditional regression approach that are most relevant in today's machine learning world. The assignments are well designed and may take some efforts to complete, but they are worth the time as they certainly reinforce the understanding of materials covered in the lectures.

автор: Jeyanthi T

•Aug 12, 2018

Very Informative and Technical Course...But lot of Mathematical derivations were too long. But very patiently explained.

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