Deploy Django + AI ML Face Recognition Web App in AWS

Deploy Django + AI ML Face Recognition Web App in AWS, available at $69.99, has an average rating of 4.3, with 104 lectures, 1 quizzes, based on 181 reviews, and has 3081 subscribers.

You will learn about Deploy Face Recognition Django Web App in AWS and 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 This course is ideal for individuals who are Anyone who want to learn OpenCV project or Python Developers curious about Artificial Intelligence Projects It is particularly useful for Anyone who want to learn OpenCV project or Python Developers curious about Artificial Intelligence Projects.

Enroll now: Deploy Django + AI ML Face Recognition Web App in AWS

Summary

Title: Deploy Django + AI ML Face Recognition Web App in AWS

Price: $69.99

Average Rating: 4.3

Number of Lectures: 104

Number of Quizzes: 1

Number of Published Lectures: 104

Number of Published Quizzes: 1

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 105

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $159.99

Quality Status: approved

Status: Live

What You Will Learn

Deploy Face Recognition Django Web App in AWS and 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

Who Should Attend

Anyone who want to learn OpenCV project

Python Developers curious about Artificial Intelligence Projects

Target Audiences

Anyone who want to learn OpenCV project

Python Developers curious about Artificial Intelligence Projects

Welcome to the AI and ML Enthusiast Course: Building a Face Recognition Web App with Django, Machine Learning, and Cloud Deployment on AWS!

Embark on an exciting journey into Artificial Intelligence as we delve into the realms of Computer Vision and Face Recognition within the expansive field of AI and ML. This course is designed to guide you through the entire development process of an end-to-end project, catering to both machine learning and web development enthusiasts.

Course Phases:

Phase 1: Machine Learning – Face Identity Recognition

Image processing techniques with OpenCV

Prerequisites of the course: Python installation and library setup

Face Detection using OpenCV and Deep Neural Networks

Feature extraction using deep neural networks

Training machine learning models: logistic regression, support vector machines, random forest

Combining models with a Voting Classifier (stacking method)

Model selection and hyperparameter tuning for face recognition

Phase 2: Machine Learning – Facial Emotion Recognition

Application of machine learning techniques from face identity recognition

Integration of detection and recognition models into a pipeline

Phase 3: Django Web App Development

Web application development in Django

Rendering HTML, CSS, and Bootstrap for the frontend

Backend development in Python using the MVT (Models, Views, and Templates) framework

Designing a SQLite database for the Django app

Interfacing machine learning pipeline models with the MVT framework

Styling the app using Bootstrap

Phase 4: Deployment / Production on AWS Cloud

Deployment of the Django Web App on AWS Elastic Beanstalk

Utilizing the AWS Free Tier for 12 months

Accessing the app globally through a provided URL/domain

Troubleshooting and error resolution during deployment

Course Highlights:

In-depth learning of OpenCV for image processing

Training models for Face Recognition and Facial Emotion Recognition

Django web app development with MVT framework

Integration of machine learning models into the web app

Deployment on AWS Elastic Beanstalk with a focus on the AWS Free Tier

If you aspire to be an AI developer, this course is your gateway to mastering AI and ML concepts while gaining hands-on experience. Don’t miss out – start your journey now!

See you inside the course!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What you will Develop ?

Lecture 3: Facing any Issue with the Course? Dont Panic. Here is the solution

Chapter 2: Setting Up Course

Lecture 1: Install Python

Lecture 2: Download requirements.txt

Lecture 3: Install CMake & Dlib on Windows

Chapter 3: OpenCV Crash Course

Lecture 1: Download the Syntax commands commonly used

Lecture 2: Download Resources

Lecture 3: What will you learn ?

Lecture 4: What is Pixel in Image.

Lecture 5: Load Image

Lecture 6: Display Image

Lecture 7: Save Image

Lecture 8: Acessing Pixels

Lecture 9: Manipulate Pixels

Lecture 10: Color Space – Split BGR

Lecture 11: Color Space – Convert Colors

Lecture 12: Drawings – Line

Lecture 13: Drawings – Line part2

Lecture 14: Drawings – Rectangle

Lecture 15: Drawings – Polygon

Lecture 16: Drawings – Circles

Lecture 17: Put Text

Lecture 18: What you will Learn ?

Lecture 19: Download the Resources

Lecture 20: Viola-Jones Object Detection Intuition

Lecture 21: Download Cascade Classifier

Lecture 22: Face Detection with Cascade Classifier

Lecture 23: Multiple Faces Detection

Lecture 24: Eyes Detection

Lecture 25: Smile Detection

Lecture 26: What you will Learn ?

Lecture 27: Download the Resourses

Lecture 28: Face Detection with Deep Neural Network Framework

Lecture 29: Face Detection with DNN part-1

Lecture 30: Face Detection with DNN part -2

Lecture 31: Face Detection with DNN part-3

Lecture 32: Feature Extraction Framework

Lecture 33: Facial Feature Extraction: part-1

Lecture 34: Facial Feature Extraction: part-2 (Landmark Detection)

Lecture 35: Facial Feature Extraction: part-3 (Face Descriptors)

Chapter 4: Practice Test

Chapter 5: Phase-1: Face Recognition Project (Person Identity)

Lecture 1: Project phase -1, Face Recognition

Lecture 2: Face Recognition Framework

Lecture 3: How to Download the Resource

Lecture 4: Download the Resource

Lecture 5: Data Preprocessing

Lecture 6: Data Preprocessing – face detection

Lecture 7: Data Preprocessing – feature extraction

Lecture 8: Data Preprocessing – Helper Function

Lecture 9: Data Preprocessing – Feature Embedding and Labeling

Lecture 10: Save Data in Pickle file

Lecture 11: Machine Learning – Data

Lecture 12: Machine Learning and Evaluation – Logistic Regression, Accuracy and F1score

Lecture 13: Machine Learning & Evaluation – Support Vector Classifier, Accuracy and F1score

Lecture 14: Machine Learning & Evaluation – Random Forest, Accuracy and F1score

Lecture 15: Machine Learning & Evaluation – Voting Classifier

Lecture 16: Grid Search Parameter Tuning

Lecture 17: Save Face Recognition Model in Pickle

Chapter 6: Facial Emotion Recognition

Lecture 1: Get the Data

Lecture 2: Download the Resources

Lecture 3: Data Preprocessing

Lecture 4: Train Machine Learning Model

Chapter 7: Pipeline All Models

Lecture 1: Load all Face Recognition and Detection Machine Learning Models

Lecture 2: Automatic Multiple Faces Detections

Lecture 3: Combine Predictions of all Machine Learning Models

Lecture 4: Create Function for Entire Code

Chapter 8: Phase-2: Setting Up Web App Project

Lecture 1: Phase-2: Django Web App

Lecture 2: Install Visual Studio Code

Lecture 3: Setting Up Visual Studio Code

Lecture 4: Create Virtual Environment from Visual Studio Code (Windows)

Lecture 5: Install & Freeze Requirements

Chapter 9: Django Basics

Lecture 1: Your First Django App

Lecture 2: Django Overview

Lecture 3: HttpRespones in Django

Lecture 4: Templates

Lecture 5: Static

Chapter 10: Face Recognition Webapp with Django

Lecture 1: Model Views Templates + Machine Learning Framework

Lecture 2: Download the Django Project

Lecture 3: Upload Images into Models – part-1

Lecture 4: Connect Models, Views, Template part-2

Lecture 5: Connect Models, Views, Template part-3

Lecture 6: Import Machine Learning Models in Django App

Lecture 7: Get Face Recognition Predictions from Machine Learning in Django

Lecture 8: Display Face Recognition Output in Django Templates

Lecture 9: Display Face Recognition Output in Django Templates part-2

Chapter 11: Styling Django WebApp with Bootstrap (CSS)

Lecture 1: Styling Django Web App with Bootstrap

Lecture 2: Styling Django Web App with Bootstrap part2

Lecture 3: Styling Django Web App with Bootstrap part3

Instructors

G Sudheer
Instructor

datascience Anywhere
Team of Engineers

Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 7 votes

2 stars: 1 votes

3 stars: 15 votes

4 stars: 54 votes

5 stars: 104 votes

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