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@@ -26,6 +26,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
     - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
     - [How to Make an Image Classifier in Python using Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier))
+    - [How to Use Transfer Learning for Image Classification using Keras in Python](https://www.thepythoncode.com/article/use-transfer-learning-for-image-flower-classification-keras-python). ([code](machine-learning/image-classifier-using-transfer-learning))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
 
 - ### [General Python Topics](https://www.thepythoncode.com/topic/general-python-topics)
@@ -46,3 +47,4 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
     - [How to Extract Weather Data from Google in Python](https://www.thepythoncode.com/article/extract-weather-data-python). ([code](web-scraping/weather-extractor))
     - [How to Download All Images from a Web Page in Python](https://www.thepythoncode.com/article/download-web-page-images-python). ([code](web-scraping/download-images))
 
+For any feedback, please consider pulling requests.
@@ -0,0 +1,22 @@
+# [How to Use Transfer Learning for Image Classification using Keras in Python](https://www.thepythoncode.com/article/use-transfer-learning-for-image-flower-classification-keras-python)
+To run this:
+- `pip3 install -r requirements.txt`
+- To train the model (already trained and the optimal weights are in `results` folder):
+    ```
+    python train.py
+    ```
+    This will load the flower dataset, construct the `MobileNetV2` model with its weights and starts training.
+- 86% accuracy was achieved on 5 classes of flowers which are `daisy`, `dandelion`, `roses`, `sunflowers` and `tulips`.
+- To evaluate the model as well as visualizing different flowers and its corresponding predictions:
+    ```
+    python test.py
+    ```
+    This will **Outputs:**
+    ```
+    23/23 [==============================] - 6s 264ms/step
+    Val loss: 0.5659930361524
+    Val Accuracy: 0.8166894659134987
+    ```
+    and **plots**:
+    ![Predicted Flowers](predicted-flowers.png)
+    Check the [tutorial](https://www.thepythoncode.com/article/use-transfer-learning-for-image-flower-classification-keras-python) for more information.
@@ -0,0 +1,3 @@
+tensorflow
+keras
+numpy