diff --git a/machine_learning/similarity_search.py b/machine_learning/similarity_search.py
index 2f5fc46c0..72979181f 100644
--- a/machine_learning/similarity_search.py
+++ b/machine_learning/similarity_search.py
@@ -12,6 +12,7 @@ from __future__ import annotations
 import math
 
 import numpy as np
+from numpy.linalg import norm
 
 
 def euclidean(input_a: np.ndarray, input_b: np.ndarray) -> float:
@@ -135,6 +136,22 @@ def similarity_search(
     return answer
 
 
+def cosine_similarity(input_a: np.ndarray, input_b: np.ndarray) -> float:
+    """
+    Calculates cosine similarity between two data.
+    :param input_a: ndarray of first vector.
+    :param input_b: ndarray of second vector.
+    :return: Cosine similarity of input_a and input_b. By using math.sqrt(),
+             result will be float.
+
+    >>> cosine_similarity(np.array([1]), np.array([1]))
+    1.0
+    >>> cosine_similarity(np.array([1, 2]), np.array([6, 32]))
+    0.9615239476408232
+    """
+    return np.dot(input_a, input_b) / (norm(input_a) * norm(input_b))
+
+
 if __name__ == "__main__":
     import doctest