# Perceptron Learning Rule

I am new to Machine Learning and Data Science. By spending some time online, I was able to understand the perceptron learning rule fairly well. But I am still clueless about how to apply it to a set of data. For example we may have the following values of $$x_1$$, $$x_2$$ and $$d$$ respectively:-

\begin{align}&(0.6 , 0.9 , 0)\\ &(-0.9 , 1.7 , 1)\\ &(0.1 , 1.4 , 1)\\ &(1.2 , 0.9 , 0)\end{align}

I can't think of how to begin.

I think we need to follow these rules.

$$W_i = W_i + \Delta W_i$$ $$\Delta W_i = \eta(d_i - y_i)$$ $$\text{ If} y_i = \sum w_ix_i \ge 0, y = 1 \text{ else} y=0$$ $$x_0 (\text{Bias}) = 0$$

Where $$d_i$$ is the target value, $$y_i$$ is the output value $$\eta$$ is the learning rate and $$x_i$$ is the input value

Any help is appreciated. Thanks!