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    Face Recognition Web App with Machine Learning Django Heroku

    Posted By: ELK1nG
    Face Recognition Web App with Machine Learning Django Heroku

    Face Recognition Web App with Machine Learning Django Heroku
    Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 2.92 GB | Duration: 7h 4m

    Develop & Deploy Face Recognition, Facial Emotion using OpenCV, Machine Learning, Django & Database in Python in Heroku

    What you'll learn
    Deploy Face Recognition Django Web App in Heroku Cloud
    Train your own Machine Learning based Face Recognition Model in Python
    Train own Facial Emotion Recognition using Machine Learning in Python
    Develop Django Web App using MVT Framework
    Design SQLlite Database in Django
    Train Support Vector Machines, Random Forest Model for Face Recognition in Python
    Debuging error while Deploying in Heroku
    Interphase Machine Learning Models with MVT Framework
    Build Ensemble (stacking) Machine Learning Model combining SVM and Random Forest Models in Python
    Face Detection with Deep Neural Networks
    OpenCV Essentials for Face Recognition
    Managing Heroku Cloud
    Styling Django Web App with Bootstrap

    Description
    Welcome to the Course Deploy Face Recognition Web App, Machine Learning, Django & Database in Heroku Cloud which is an Artificial Intelligence Project.

    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?

    Prerequisite of Project: OpenCV

    Image Processing with OpenCV

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

    Feature Extraction with OpenCV and Deep Learning Networks

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    Project Phase - 1: Face Recognition and Person Identity

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    Gather Images

    Extract Faces only from Images

    Labeling (Target output) Images

    Data Preprocessing

    Training Face Recognition with OWN Machine Learning Models.

    Logistic Regression

    Support Vector Machines

    Random Forest Classifier

    Combine All Machine Learning Models with Voting Classifier

    Tuning Machine Learning Model

    Model Evaluation

    Precision

    Recall

    Sensitivity

    Specificity

    F1 Score

    Accuracy

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    Project Phase - 2: Train Facial Emotion Recognition

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    Gather Emotion Images

    Data Preprocessing

    Train Machine Learning Models

    Tuning Machine Learning Models

    Model Evaluation

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    Project Phase -3: Django Web App Developed in Local (Computer)

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    Setting Up Visual Studio Code

    Install all Dependencies of VS Code

    Setting Virtual Environment

    Freeze Requirements

    Learn Django Basics

    SETTINGS

    URLS

    VIEWS

    TEMPLATES (HTML)

    Face Recognition Django Project

    Models Views Templates (MVT)

    Design SQLite Database in Django

    Store Uploaded Image in Database

    Integrate Machine Learning to Django

    MVT + Machine Learning Framework

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    Styling Django Web App with Bootstrap

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    Project Phase -4: Deploy Web App in Heroku Cloud for Production

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    Setting up Heroku Account.

    Creating App in Heroku

    Install Heroku CLI, GIT

    Deploy Heroku in Cloud

    Necessary Installation to Fix CSS 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 using deep neural networks then with the features of faces, 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 Django by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Then, we will create the project on the Face Recognition project by integrating the machine learning model to Django App.

    Finally we will deploy this entire Django Web App in Heroku Cloud for production and get URL/domain where you can access anywhere in the world.

    We know that Face Recognition with Django and Deployed in Heroku leaves you will questions and error. Don't worry we are here are there to answer all the question and help you to complete the course.