Image Classification - End to End Machine Learning Project | From Data Gathering to Deployment

End to End Machine Learning Project on Image Classification

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Creating dataset using Bing/ Google Image search APIs and then labelling them simplifies the entire process, and adds flexibility to the flow of machine learning for the Data Gathering.

Deployment is an important part in any Data Science project as well which is most often neglected. Build a web App using Streamlit.

Learn how to create a complete End to End Machine Learning project on Image Classification by watching this video.

Put your doubts if any in comment section. We will try answering them.

#diazoniclabs #imageclassification #machinelearning #teachablemachine

Steps involved in Image Classification - Live End to End Machine Learning Project :

1. Introduction to Machine Learning : 16:00
2. What is Google Colab? : 26:26
3. Demonstration of Teachable Machine : 32:10
4. Bing Image Downloader 43:00
5. Data Preprocessing - 58:29
6. Hyperparameter Tuning using GridSearchCV - 1:31:46
7. Support Vector Machine - 1:37:00
8. Evaluation of model - 1:42:00
9. Saving the model in a Pickle file - 1:45:15
10. Checking for a new image from Google - 1:47:40
11. Deployment using Streamlit - 1:57:28

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Check the Libraries version compatible with this project.
1. numpy==1.18.5
2. matplotlib==3.2.2
3. pyngrok==4.1.1
4. bing_image_downloader==1.0.4
5. scikit_image==0.16.2
6. scikit-learn==0.22.2.post1
7. Pillow==8.0.1
8. streamlit==0.70.0