案例学习：预测房价

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案例学习：预测房价

Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis

4.8（4,217 个评分）

- 5 stars3,430 ratings
- 4 stars667 ratings
- 3 stars70 ratings
- 2 stars18 ratings
- 1 star32 ratings

Apr 07, 2016

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

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.

从本节课中

Simple Linear Regression

Our course starts from the most basic regression model: Just fitting a line to data. This simple model for forming predictions from a single, univariate feature of the data is appropriately called "simple linear regression".<p> In this module, we describe the high-level regression task and then specialize these concepts to the simple linear regression case. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Based on this fitted function, you will interpret the estimated model parameters and form predictions. You will also analyze the sensitivity of your fit to outlying observations.<p> You will examine all of these concepts in the context of a case study of predicting house prices from the square feet of the house.

#### Emily Fox

Amazon Professor of Machine Learning#### Carlos Guestrin

Amazon Professor of Machine Learning

[MUSIC]

Okay, so for the first part of our course on regression,

we're gonna start with something that's called simple regression.

And as the name implies, it's just a very simple form of regression,

where we assume that we just have one input.

And we're just trying to fit a line.

Okay, but before we get to starting to talk about this simple regression model,

let's just recall our task of interest.

Where our case study is discussing how to predict house prices.

So in particular, we have some house that we wanna list for

sale, but we don't the value of this house.

And as we discussed at fairly great length in the first course of the specialization,

what we're going to do in this case,

is we're going to look at other houses that sold in the recent past.

And look at how much they've sold and different characteristics of those houses,

and use that data to inform our listing price for

our house that we'd like to sell.

[MUSIC]