Machine Learning: Regression(으)로 돌아가기

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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!

필터링 기준:

교육 기관: Olexandra Z

•Feb 05, 2017

Really great explanations for complex and important principles as well as math approaches and tools. Being a mathematician, I thought that in this math aspect there would be nothing new for me, but still it was a great refreshment and very useful explanations to understand how those methods actually work for machine learning tasks. Great balance of theory and practical applications! Thank you!

교육 기관: Michele P

•Aug 23, 2017

Very nice explanation of ridge and lasso regression. Assignments are easier than in Classification. I highly recommend this course!

교육 기관: Maria Z

•Dec 27, 2017

Much more difficult than the first course. It would be challenfing for those who don't have programming skills and math background.

교육 기관: Tariq H

•Oct 03, 2017

Thanks

교육 기관: Saheed S

•Sep 19, 2017

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.

교육 기관: Alejandro T

•Dec 04, 2016

Very thorough course and very approachable much better than course one of specialization

교육 기관: 邓松

•Dec 06, 2016

very helpful

교육 기관: Nsair A

•Mar 03, 2017

this course offers so much that by the time you are going through the lecture videos and the reading material, you do all the tasks along and you don't want the lecture to end. In fact by the time a lecture is finished, you want to do more and you click on the next one. the course gives a very good understanding of machine learning models and the skills gained can be used in a lot of different situations.

교육 기관: Mark W

•Aug 12, 2017

Excellent course. Emily and Carlos are fantastic teachers and have clearly put in a huge amount of effort in makign a great course. Thanks guys!

교육 기관: Ahmed A

•Nov 30, 2015

I was only able to complete week 1 to week 3 thoroughly, and random check on other weeks due to limited time at my disposal at this moment.

In general, I found the course to be very interesting and an excellent introduction to building predictive models . Particularly , i appreciate the way mathematical formulations was explained to carry along beginners in this areas.

Nonetheless, I would suggest that the general notation slide in week 2 should include concrete data example in a table to explain the notations ie. x[j], xi[j], etc

교육 기관: 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!

교육 기관: Chris W

•Jan 10, 2016

Another great module.

교육 기관: WEI Y

•Jul 05, 2018

Really great course! Highly recommend it!

교육 기관: Maxwell N M

•Apr 07, 2016

Lasso is very cool for dimension reduction i discover another algorithm powerfull than Personal Component Analysis

교육 기관: 童哲明

•Jun 12, 2016

Kernel regression还是有许多不太清楚的地方！

교육 기관: Adil A

•Mar 15, 2017

Very nice course... The instructors were really great, the explanations, the presentations, even the color schemes were all really great... Definitely one of the most fun courses I've taken at Coursera... The assignments were also well designed...

교육 기관: g

•Aug 29, 2016

Intuitive and very helpful, great assignment not too hard

교육 기관: Jooho S

•May 24, 2016

This course helped me a lot to understand regression. Now I can apply this idea to my own work.

교육 기관: xichen

•Mar 08, 2016

Really cool and practical

교육 기관: nazar p

•May 06, 2017

good stuff.

교육 기관: Nicolas T

•Dec 18, 2015

Best Machine learning mooc !!!

교육 기관: vivek s

•Aug 31, 2016

it's a nice course. I have learnt many new concepts. I am from information systems background and want my career towards data science. This course helped me a lot in learning new concepts.

교육 기관: Misha S

•Feb 29, 2016

Exceptionally well organized, fun and full of useful content. Bravo to the course organizers!

교육 기관: james

•Apr 27, 2016

One of the best courses I've ever taken.

교육 기관: 易灿

•Nov 28, 2016

课程很生动，讲的也很详细！如果能提供些相关算法的资料就更好了！