Complete Face Recognition App Machine Learning Django Heroku

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.74 GB | Duration: 6h 50m

Create and Deploy Face and Emotion Recognition project in Heroku Cloud with Python, OpenCV, Machine Learning & Django
What you'll learn
Face Recognition Web App project with Django using Machine Learning
Train own Face Recognition
Train own Facial Emotion Recognition
Face Detection with Deep Neural Networks
OpenCV Essential for Face Recognition
opencv

Description
Welcome to the Course Deploy Face Recognition Web App, Machine Learning, and Django in Heroku Cloud

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?

Image Processing with OpenCV

Face Detection with Viola-Jones and Deep Neural Networks (SSD)

Feature Extraction with OpenCV

Face Recognition Classification Model with

Support Vector Machine,

RandomForest,

Voting Classifier

Pipeline All Model

Django (Template, HTML, Bootstrap, HTTP Methods)

Deploy Django in 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.

Who this course is for:
Anyone who want to learn opencv
Python Developers start project on image processing with machine learning