Linear Regression

Linear regression?

Multiple linear regression

So, here's our linear regression algorithm.

So are linear regression and logistic regression different algorithms or not?

So that's linear regression with gradient descent.

And thus, using linear regression this way.

Now if you take this update rule and compare it to what we were doing for linear regression, you might be surprised to realize that, well, this equation was exactly what we had for linear regression.

But even if you don't understand it, the normal equation and linear regression, you should really get that to work okay.

Our first learning algorithm will be linear regression.

By now, you've seen a couple different learning algorithms, linear regression and logistic regression.

learning algorithm, that'll give us our linear regression algorithm.

You now know about linear regression and gradient descent.

You now know about linear regression with multiple variables.

But for this specific model of linear regression, the normal equation can give you a alternative that can be much faster, than gradient descent.

The r2 for the resulting linear regression was 0.55 (fig.

Thrun In this question I quiz you about linear regression.

Right? This was our objective function for the linear regression.

This model is called linear regression or this, for example, is actually linear regression with one variable, with the variable being x. Predicting all the prices as functions of one variable X. And another name for this model is univariate linear regression.

And in particular I also want to tell you about polynomial regression allows you to use the machinery of linear regression to fit very complicated, even very non linear functions.

So this is going to be my overall objective function for linear regression.

That's our gradient descent algorithm, which should be familiar, and you see the linear linear regression model with our linear hypothesis and our squared error cost function.

The best fit using multiple linear regression was established using the following two equations

We saw how feature scaling can help gradient descents converge faster for linear regression.

And this seems a pretty bad thing for linear regression to have done, right?

And no surprise for linear regression the cost for you to define is that.

And, just to give this another name, this is also called multivariate linear regression.

On this slide, we looked at over fitting for the case of linear regression.

The radial Kepler equation is used for linear (radial) orbits (ε 1).

Now, all of the sudden, I have a non linear differential equation.

And univariate is just a fancy way of saying one variable. So, that's linear regression.

So, applying linear regression to a classification problem usually isn't, often isn't a great idea.

In linear regression we have a training set, like maybe the one I've plotted here.

Back when we were developing the linear regression model, we use the following cost function.

In this video, we'll talk about the normal equation, which for some linear regression problems, will give us a much better way to solve for the optimal value of the parameters theta.

Now the second thing we have to figure out is this linear or is this a non linear differential equation?

In particular let's talk about how to use gradient descent for linear regression with multiple features.

For linear regression, we compute the quotation B and A, and I give you the formulas

Maybe it's a cost function from linear regression. Maybe it's some other function we want to minimize.

This article is about the Hill differential equation for the equation used in biochemistry see Hill equation (biochemistry) In mathematics, the Hill equation or Hill differential equation is the second order linear ordinary differential equation formula_1where f(t) is a periodic function.

And if you implement this you will get a working implementation of gradient descent for multivariate linear regression.

in this video we will start to talk about a new version of linear regression that's more powerful.

The linear regression will have a large number of features and for some of the other algorithms that we'll see in this course, because, for them, the normal equation method just doesn't apply and doesn't work.

So a differential equation is linear if all of the functions and its derivatives are essentially, well for lack of a better word, linear.

So, using a vectorized implementation, you should be able to get a much more efficient implementation of linear regression.