# SVM (Support Vector Machine) in Python - ML From Scratch 07

In this Machine Learning from Scratch Tutorial, we are going to implement a SVM (Support Vector Machine) 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:

## Implementation

```
import numpy as np
class SVM:
def __init__(self, learning_rate=0.001, lambda_param=0.01, n_iters=1000):
self.lr = learning_rate
self.lambda_param = lambda_param
self.n_iters = n_iters
self.w = None
self.b = None
def fit(self, X, y):
n_samples, n_features = X.shape
y_ = np.where(y <= 0, -1, 1)
self.w = np.zeros(n_features)
self.b = 0
for _ in range(self.n_iters):
for idx, x_i in enumerate(X):
condition = y_[idx] * (np.dot(x_i, self.w) - self.b) >= 1
if condition:
self.w -= self.lr * (2 * self.lambda_param * self.w)
else:
self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_[idx]))
self.b -= self.lr * y_[idx]
def predict(self, X):
approx = np.dot(X, self.w) - self.b
return np.sign(approx)
```

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# TensorFlow 2 Beginner

Learn all the necessary basics to get started with TensorFlow 2 and Keras.

# PyTorch Beginner

Learn all the necessary basics to get started with this deep learning framework.

# ML From Scratch

Implement popular Machine Learning algorithms from scratch using only built-in Python modules and numpy.