Welcome to the Course Deploy Face Recognition Web App, Machine Learning, Django & Database in Heroku Cloud !!!.
An Artificial Intelligence Project.
Computer Vision & Face recognition is one of the most widely used in the area of Artificial Intelligence and Data Science. If at all you want to develop an end-to-end application in Data Science, then you need to be a master in Machine Learning / Deep Learning, and in addition to that, you need to have knowledge in Web Development.
This course is one stop course where you will learn End to End development of a Computer-Vision Based Artificial Intelligence Project from SCRATCH. As this course is a full-stack course we designed this course into 4 phases
Phase-1: Machine Learning - Face Identify Recognition
In this phase, we majorly cover the practical concepts related to machine learning models like data preprocessing, analysis, training machine learning, and model evaluation and selection (Grid Search Hyperparameter Tuning)
Here I will teach you how to develop face recognition models using machine learning
Phase-2: Machine Learning - Facial Emotion Recognition
Phase-3: Django Web App Development
In this phase, I will teach you how to develop a Web App with Django.
We will use a powerful framework which is the MVT (Models Views Templates) framework to develop the web app.
You will also learn how to design a database (SQLite) for the Web App in Django.
Integrate Machine Learning Model to MVT framework
I will also explain, styling using Bootstrap
Phase-4: Deployment / Production
In this phase, we will deploy the Django web app on a cloud platform which is the HEROKU cloud
I will explain all the necessary steps and installation to deploy the Django Project
If you want to become an AI developer this is the perfect course to starts with. Below given is the high-level abstract of the course and the learning objectives.
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
Project Phase - 1: Face Recognition and Person Identity
Extract Faces only from Images
Labeling (Target output) Images
Training Face Recognition with OWN Machine Learning Models.
Support Vector Machines
Random Forest Classifier
Combine All Machine Learning Models using Ensemble Technique with Voting Classifier
Tuning Machine Learning Model
Project Phase - 2: Train Facial Emotion Recognition
Gather Emotion Images
Train Machine Learning Models
Tuning Machine Learning Models
Project Phase -3: Django Web App Developed in Local (Computer)
Setting Up Visual Studio Code
Install all Dependencies of VS Code
Setting Virtual Environment
Learn Django Basics
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
Styling Django Web App with Bootstrap
Project Phase -4: Deploy Web App in Heroku Cloud for Production
Setting up Heroku Account.
Creating App in Heroku
Install Heroku CLI, GIT
Deploy Heroku in Cloud
Necessary Installation to Fix CSS in Heroku.
I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.
With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Machine learning models like logistic regression, support vector machines, random forest. Then we combine all machine learning models with Voting Classifier (stacking method). I will teach you the model selection and hyperparameter tuning for face recognition models
In Phase-2, we will apply the machine learning techniques used in face identity recognition for facial emotion recognition. After that, we will combine all different detection and recognition models into a pipeline.
Once our machine learning model is ready, will we move to Phase-3, and develop a Web Application in Django by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Here I will teach you the necessary prerequisite of Django. Then we will develop a web app using the MVT (Models, Views, and Templates) framework. We will start developing Django App by designing a database in SQLite. Here I will also teach you to interphase machine learning pipeline models to the MVT framework. In the end, we will style our app using Bootstrap.
Finally, we will deploy the entire Django Web App in Heroku Cloud for production and get a URL/domain where you can access it anywhere in the world. I will also teach all the necessary installation required and explain how to solve errors whenever you have encountered them while deploying your web app.
What are you waiting for? Start the course develop your own Computer Vision Django Web Project using Machine Learning, Python and Deploy it in Cloud with your own hands.
I will see you inside the course.