What you'll learn:
- Automatic Face Recognition in images and videos
- Automatically detect faces from images and videos
- Evaluate and Tune Machine Learning
- Building Machine Learning Model for Classification
- Make Pipeline Model for deploying your application
- Image Processing with OpenCV
- Data Preprocessing for Images
- Create REST APIs in Flask
- Template Inheritance in Flask
- Integrating Machine Learning Model in Flask App
- Deploy Flask App in Heroku Cloud
MLOPs: AI based Face Recognition Web App in Flask & Deploy
Face recognition is one of the most widely used in my application. If at all you want to develop and deploy the application on the web only knowledge of machine learning or deep learning is not enough. You also need to know the creation of pipeline architecture and call it from the client-side, HTTP request, and many more. While doing so you might face many challenges while developing the app. This course is structured in such a way that you can able to develop the face recognition based web app from scratch.
What you will learn?
Python
Image Processing with OpenCV
Image Data Preprocessing
Image Data Analysis
Eigenfaces with PCA
Face Recognition Classification Model with Support Vector Machines
Pipeline Model
Flask (Jinja Template, HTML, CSS, HTTP Methods)
Develop Face Recognition Web
Deploy Flask App in Cloud (Heroku)
You will learn image processing techniques in OpenCV and the concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for images.
For the preprocess images, we will extract features from the images, ie. computing Eigen images using principal component analysis. With Eigen images, we will train the Machine learning model and also learn to test our model before deploying, to get the best results from the model we will tune with the Grid search method for the best hyperparameters.
Once our machine learning model is ready, will we learn and develop a web server gateway interphase in flask by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Finally, we will create the project on the Face Recognition project by integrating the machine learning model to Flask App.
