Machine Learning: Regression に戻る

4.8

4,231件の評価

•

804件のレビュー

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 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

by 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.

by 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..

by Patrick M d F

•Jul 05, 2019

Excellent trad-off between theory, algorithims and practical examples

by Lucifer Z

•Jul 05, 2019

awesome ML course!

by Kevin

•Jun 28, 2019

Clear explanation on regularization as well as bias-variance trade-off.

by Thuc D X

•Jun 18, 2019

The program assignment's description was written badly and hard to follow

For example: in week 6's assignment, the description doesn't indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn't use.

by Naman M

•Jun 15, 2019

It is the best course on the coursera for machine learning

by Yufeng X

•Jun 14, 2019

Best lectures!

by Giampiero M

•Jun 14, 2019

great course, with more relevant technical infos

by Md s

•Jun 09, 2019

Awesome Course , really helpful to do things from scratch

by Santosh K D

•Jun 05, 2019

Professor Emily Fox should do a follow up for this course. It was so simple and intuitive to understand. I want to work as a PhD student under her.

by Carin N

•Jun 05, 2019

The courses get better and have more assistance for those of us who can't / didn't use graph lab. It is still outdated as python 3 came out after the course was created. But did learn a lot of stuff. Module 4 was the most frustrating as you'll get the wrong answers if you use pandas/sklearn.

by Aakash S

•Jun 05, 2019

Amazing Course. Thanks.

by Xi C

•May 22, 2019

Very intuitive explanations!

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 Oscar S

•May 16, 2019

Step by Step about Regression explained well and easy to understand. Mandatory course for every data science begginer.

by Dohyoung C

•May 11, 2019

Thank you for a good lecture.

The material was excellent and explanation was quite detailed and easy to understand.

Some of the programming was a little bit tricky, but I was able to pull through.

Thank you again for your efforts and I am looking forward to seeing you in the next course

by Vansh S

•May 10, 2019

nice

by Nikhil P

•May 01, 2019

Great course, great material

by MAO M

•Apr 29, 2019

Very good for beginners

by Mukul k

•Apr 22, 2019

excellent course . lots of interesting things i have learned

by Nipun G

•Apr 21, 2019

Please get rid of SFrame and graphlab. However, professor is awesome!

by Gabriele P

•Apr 16, 2019

The program is well structured, the lessons are interesting and the hands on nice. However, the instructor should really consider to update their material to python 3 + turicreate. Python 2 is reaching EOL in 2020 and should be avoided for teaching/training. I did most of my notebooks with python 3 and turicreate, it is really worth the effort to update the material. The tests are ok, but some looked somewhat buggy (as reported in the forum by many users) and could use a revision

by Martin B

•Apr 11, 2019

Excellent explanation of the use of regression-based Machine Learning techniques. I recommend taking the specialization on Machine Learning Mathematics before taking this one - it will give you a deeper understanding of some of the mathematical concepts involved and make for a greater experience with this course. Programming assignments are good and help the learner with applying and re-visiting the material. Big drawback is the insistence in most of the assignments on using Python 2 and Graphlab Create. Workarounds for users of Pandas, Scikit-Learn, NLTK etc. are provided but it could be better.