The final part of the basic supervised machine learning trinity is the gradient descent algorithm. Given a hypothesis and a cost function, this algorithm iterates through different values of ‘theta,’ (remember this is a parameter set), to find something called the local optima for a particular data set. Right, but that explanation is […]

# Tag: linear regression

The very first formula I learned in machine learning (and the first time I tried writing in LaTeX!) So pretty cool, but what does it mean? This is an example of a univariate hypothesis. ‘Univariate’ is a fancy way of saying that I have one variable (let’s call it ‘x’ for now) that […]

Linear regression, cost function and gradient descent sum up the first two weeks of Professor Ng’s Machine Learning class on Coursera. Since I am not aspiring to be a mathematician, I am going to refer to them as algorithms. Each of these algorithms has a particular place when designing a machine learning solution. Linear regression, […]