The problem of overfitting So far we’ve seen a few algorithms – work well for many applications, but can suffer from the problem of overfitting What is overfitting? What is regularization and how does it help Overfitting with linear regression Using our house pricing example again Fit a linear function Read more…
Classification Where y is a discrete value Develop the logistic regression algorithm to determine what class a new input should fall into Classification problems Email -> spam/not spam? Online transactions -> fraudulent? Tumor -> Malignant/benign Variable in these problems is Y Y is either 0 or 1 0 = negative class (absence of something) Read more…
Linear regression with multiple features Multiple variables = multiple features In original version we had X = house size, use this to predict y = house price If in a new scheme we have more variables (such as number of bedrooms, number floors, age of the home) x1, x2, x3,x4 are the four features Read more…
Matrices – overview Rectangular array of numbers written between square brackets 2D array Named as capital letters (A,B,X,Y) Dimension of a matrix are [Rows x Columns] Start at top left To bottom left To bottom right R[r x c] means a matrix which has r rows and c columns Is a [4 Read more…
Introduction to the course We will learn about State of the art How to do the implementation Applications of machine learning include Search Photo tagging Spam filters The AI dream of building machines as intelligent as humans Many people believe best way to do that is mimic how humans learn What the course covers Learn about state Read more…