In this Machine Learning from Scratch Tutorial, we are going to implement the Linear Regression 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://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html
- https://ml-cheatsheet.readthedocs.io/en/latest/gradient_descent.html

## Implementation

```
import numpy as np
class LinearRegression:
def __init__(self, learning_rate=0.001, n_iters=1000):
self.lr = learning_rate
self.n_iters = n_iters
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
# gradient descent
for _ in range(self.n_iters):
y_predicted = np.dot(X, self.weights) + self.bias
# compute gradients
dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
db = (1 / n_samples) * np.sum(y_predicted - y)
# update parameters
self.weights -= self.lr * dw
self.bias -= self.lr * db
def predict(self, X):
y_approximated = np.dot(X, self.weights) + self.bias
return y_approximated
```