Machine Learning: Regression に戻る

4.8

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793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....

by PD

•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!

by CM

•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!

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763件のレビュー

by Oscar Salgado

•May 16, 2019

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

by Jafed Encinas

•May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

by Dohyoung Chung

•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 Srivastava

•May 10, 2019

nice

by Nikhil Pandey

•May 01, 2019

Great course, great material

by MAO MAO

•Apr 29, 2019

Very good for beginners

by Mukul kumar

•Apr 22, 2019

excellent course . lots of interesting things i have learned

by Nipun Goel

•Apr 21, 2019

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

by Gabriele Penazzi

•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 Belder

•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.