In this Machine Learning from Scratch Tutorial, we are going to implement a single-layer Perceptron algorithm using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

### Further readings:

https://sebastianraschka.com/Articles/2015_singlelayer_neurons.html

## Implementation

```
import numpy as np
class Perceptron:
def __init__(self, learning_rate=0.01, n_iters=1000):
self.lr = learning_rate
self.n_iters = n_iters
self.activation_func = self._unit_step_func
self.weights = None
self.bias = None
def fit(self, X, y):
n_samples, n_features = X.shape
# init parameters
self.weights = np.zeros(n_features)
self.bias = 0
y_ = np.array([1 if i > 0 else 0 for i in y])
for _ in range(self.n_iters):
for idx, x_i in enumerate(X):
linear_output = np.dot(x_i, self.weights) + self.bias
y_predicted = self.activation_func(linear_output)
# Perceptron update rule
update = self.lr * (y_[idx] - y_predicted)
self.weights += update * x_i
self.bias += update
def predict(self, X):
linear_output = np.dot(X, self.weights) + self.bias
y_predicted = self.activation_func(linear_output)
return y_predicted
def _unit_step_func(self, x):
return np.where(x>=0, 1, 0)
```