diff --git a/computer_vision/haar_classifier.py b/computer_vision/haar_classifier.py
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+"""
+Haar Cascading Classifiers
+
+Objective : Detect Object (Face)
+
+Resources Haar Classifiers :
+https://www.analyticsvidhya.com/blog/2022/04/object-detection-using-haar-cascade-opencv/#:~:text=Haar%20cascade%20is%20an%20algorithm,%2C%20buildings%2C%20fruits%2C%20etc
+https://machinelearningmastery.com/using-haar-cascade-for-object-detection/
+Resource for Haar classifiers: https://github.com/opencv/opencv/tree/master/data/haarcascades
+
+Download photo from :
+https://unsplash.com/
+
+1. Open Colab and create new notebook.
+2. Download a free to download photo from unsplash or use one of your own.
+3. Upload the photo in the model
+4.  Use haar classifier to detect face in image
+"""
+
+from google.colab.patches import (
+    cv2_imshow,
+)  # to assist with image processing and showing
+import cv2
+import numpy as np
+
+
+# Defining the list of image URLs from Unsplash
+foto = ["https://spash.com/[chosen photo URL]" "https://splash.com/[chosen photo URL]"]
+
+
+def load_image_from_url(url):
+    resp = urlopen(url)
+    image = np.asarray(bytearray(resp.read()), dtype="uint8")
+    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+    return image
+
+
+for i, image_url in enumerate(foto):
+    img = load_image_from_url(image_url)
+    print(f"Displaying image {i+1}")
+    cv2_imshow(img)
+# The above code should be in one cell and display the photo you chose.
+
+###########next cell##############
+
+# Loading the pre-trained Haar cascade for face detection
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)  # you can use others but this one is used the most.  Resource link above.
+
+
+# detecting the face and covert to grayscale
+def detect_faces(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(
+        gray, scaleFactor=1.1, minNeighbors=20, minSize=(20, 20)
+    )  # Setting general parameters.  Resouces above to understand more.
+    for x, y, w, h in faces:
+        cv2.rectangle(
+            image, (x, y), (x + w, y + h), (255, 0, 0), 2
+        )  # blue colour bounding box
+    return image
+
+
+# Processing each loaded and resized image with Haar cascade
+for i, url in enumerate(foto, start=1):
+    img = load_image_from_url(url)
+    img_with_faces = detect_faces(img.copy())
+    print(f"Detecting Face {i}")
+    cv2_imshow(img_with_faces)  # face
+
+# this should display the image with a blue bounding box.