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....

创建者 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!

创建者 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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755 个审阅

创建者 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

创建者 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.

创建者 Ling Zheng

•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.

创建者 Neelkanth Sanjay Mehta

•Apr 08, 2019

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

创建者 Tahereh Rashnavadi

•Apr 02, 2019

Thorough explanations of the essential concepts are provided! Valuable course and lectures.

Thanks!

创建者 Jenhau Chen

•Mar 31, 2019

Great course! Very good insight!

创建者 akashkr1498

•Mar 28, 2019

please take care while framing assignment and quize question it is very difficult to understand what exactly u want us to do

创建者 kripa shankar

•Mar 25, 2019

I must say it was great learning experiance. Everything releted to ML regression has been covered so eloquently.

创建者 YASHKUMAR RAMESHBHAI TRADA

•Mar 24, 2019

The mathematical proof and concept given behind lasso and ridge regression is awesome.

创建者 Akash Gupta

•Mar 09, 2019

regression best now