Warezcrackfull.com » Tutorial » Deploy Django + AI ML Face Recognition Web App in AWS

Deploy Django + AI ML Face Recognition Web App in AWS

Author: warezcrackfull on 15-11-2024, 01:26, Views: 0

Free Download Deploy Django + AI ML Face Recognition Web App in AWS
Last updated 1/2024
Created by G Sudheer,datascience Anywhere,Brightshine Learn
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 104 Lectures ( 8h 14m ) | Size: 3.76 GB

Develop & Deploy Face Recognition, Facial Emotion using OpenCV, Machine Learning, Django, Database in Python in AWS
What you'll 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
Requirements
Should be at least beginner to Python
Basic knowledge in Machine Learning
Understanding HTML, Bootstrap
Should know how to install Python Libraries using pip
Description
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 RecognitionImage processing techniques with OpenCVPrerequisites of the course: Python installation and library setupFace Detection using OpenCV and Deep Neural NetworksFeature extraction using deep neural networksTraining machine learning models: logistic regression, support vector machines, random forestCombining models with a Voting Classifier (stacking method)Model selection and hyperparameter tuning for face recognitionPhase 2: Machine Learning - Facial Emotion RecognitionApplication of machine learning techniques from face identity recognitionIntegration of detection and recognition models into a pipelinePhase 3: Django Web App DevelopmentWeb application development in DjangoRendering HTML, CSS, and Bootstrap for the frontendBackend development in Python using the MVT (Models, Views, and Templates) frameworkDesigning a SQLite database for the Django appInterfacing machine learning pipeline models with the MVT frameworkStyling the app using BootstrapPhase 4: Deployment / Production on AWS CloudDeployment of the Django Web App on AWS Elastic BeanstalkUtilizing the AWS Free Tier for 12 monthsAccessing the app globally through a provided URL/domainTroubleshooting and error resolution during deploymentCourse Highlights:In-depth learning of OpenCV for image processingTraining models for Face Recognition and Facial Emotion RecognitionDjango web app development with MVT frameworkIntegration of machine learning models into the web appDeployment on AWS Elastic Beanstalk with a focus on the AWS Free TierIf 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!
Who this course is for
Anyone who want to learn OpenCV project
Python Developers curious about Artificial Intelligence Projects
Homepage

https://www.udemy.com/course/complete-face-recognition-app-machine-learning-django-heroku/

Screenshot

Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

Your name:*
E-Mail:

Enter the code: *