Machine Learning: Regression に戻る

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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!

フィルター：

by Fernando F

•Jan 12, 2016

I think this course has been very interesting. Regression is too wide for covering entirely in a course like this but it has provided me with the basic knowledge and fundamentals to keep working in the matter.

by Stephen M

•Jan 23, 2018

I enjoyed the math and the Python exercises, which were interesting and challenging. The functions and algorithms used in the notebooks would be a good starting point for a set of Python regression classes.

by Mohamed A M A E

•Oct 17, 2017

it is a good contant and i learn more information such as

Simple linear regression, Multiple regressionAssessing , performanceRidge , regressionFeature selection & LassoNearest , neighbor & kernel regression

by Alfred G

•Jun 29, 2016

I strongly recommended you guys to walk through this course. It worth it! And the programming assignment is awesome. I also recommended that you can try to use sklearn + pandas + numpy to rebuild your code.

by 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 .

by Syed T U S

•Jun 28, 2018

This course is amazing and cover a wide range of topics. It broadband my knowledge in the core area of machine learning. The course content and teaching style is tremendous. Thank you Coursera and UoW.

by Eftychios V

•Jun 25, 2016

An in-depth overview of the regression techniques and models. I think it went as deep into the concepts as I wanted it to go. Being a developer I found it quite understandable, and useful.

Keep it up!

by Rafael R d S

•Nov 30, 2016

Excelent course, I highly recommend for those who are willing to learn machine learning from the basis, this module (linear regression) covered very important parts that I used to struggle to learn

by Fede R

•Jan 02, 2017

This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.

by Chia-Sheng L

•Jan 04, 2016

This course offer many aspects like graphic comparison or detail math explanation help us understand more easily what a model or method means. The teacher have great effort on material design.

by Prashant M

•Sep 30, 2017

This was a very satisying course with the intensity and queries that challenge me and wish to learn more. I am quite excited to learn more with the new ML bug that has caught me! Liberating.

by Wenxin X

•Mar 12, 2016

Learned a lot! Now I have been acquired a basic understanding of machine learning! Materials are not much, so it's not painful to accept. Recommended for everybody interested in this topic!

by Gustavo K A

•Jan 08, 2016

I had the clear sense of actually learning and not just "copying & pasting" bits of code. The questions and problems are challenging enough to make you stop and think about you just learned.

by Sergey M

•Jan 19, 2016

A very good course! Especially that scikit-learn can be used as framework for solving assignments and at the same time exercises for programming of learning algorithms from scratch. Thanks!

by Bilkan E

•Oct 16, 2016

Incredible course!

Very good, intuitive and simple introduction to general use machine learning and optimization techniques. I am already employing techniques learned here to my daily work.

by 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.

by Sekhar K

•Apr 02, 2017

This course is phenomenal! I am learning a great deal. Dr. Emily Cox is fantastic with her slides, explanation and the way she (and Dr. Carlos Guestrin) structured the course. Loving it!

by Ling Z

•Apr 09, 2019

I took this class long time ago and just revisited it today. Compared to other online class, this class has a lot details. I am satisfied with both the clarity and depth of the content.

by Fabio P

•Apr 03, 2016

I really like the learning approach in this course: at first you learn how to use the algorithm and after that you learn how to implement it yourself. That way it's never disappointing.

by George P

•May 16, 2017

Straight to the point and with useful material to check back whenever you feel is necessary. Learning but also good annotated notes in order to revise things later are very important.

by Zachary C

•Apr 30, 2017

the professor does an excellent job explain the subject thoroughly, including good in depth descriptions of matrix algebra and how it applies to things like multi-variable regression.

by Alexander T

•Dec 30, 2015

A very comprehensive course that covers not only regression, but all base Machine Learning concept. Thanks to Emily, she explains rather complicated topics in a clear and concise way.

by Ben K

•Feb 23, 2016

Lasso, l2 regularization, ridge regression, etc. - appropriate level of technical detail, first principles discussion, etc. means that a lot of good info was packed into this course.

by Vibhutesh K S

•May 20, 2019

This is indeed a good course. Covering even much more than I had previously expected. The instructions were quite clear to me and the programming assignments were quite interesting.

by Nitish V

•Sep 25, 2017

The course is really good for people planning to step into machine learning field. Not so deep , but covers all the relevant topics. Thanks to instructor for making it look so easy.