Complete Face Recognition App Machine Learning Django Heroku

欢迎学习在 Heroku Cloud 中部署人脸识别 Web 应用程序、机器学习和 Django 的课程

人脸识别是我的应用中使用最广泛的一种。如果您想在网络上开发和部署应用程序，仅有机器学习或深度学习的知识是不够的。您还需要了解管道架构的创建并从客户端调用它、HTTP 请求等等。这样做时，您在开发应用程序时可能会面临许多挑战。本课程的结构使您能够从头开始开发基于人脸识别的 Web 应用程序。

你会学到什么？

使用 OpenCV 进行图像处理

使用 Viola-Jones 和深度神经网络 (SSD) 进行人脸检测

使用 OpenCV 进行特征提取

人脸识别分类模型

支持向量机，

随机森林，

投票分类器

管道所有型号

Django（模板、HTML、Bootstrap、HTTP 方法）

在 Heroku 中部署 Django

您将学习 OpenCV 中的图像处理技术以及图像背后的概念。我们还将对图像进行必要的图像分析和所需的预处理步骤。

对于预处理图像，我们将从图像中提取特征，即。使用主成分分析计算特征图像。使用特征图像，我们将训练机器学习模型并学习在部署之前测试我们的模型，为了从模型中获得最佳结果，我们将使用网格搜索方法进行调整以获得最佳超参数。

一旦我们的机器学习模型准备就绪，我们将通过在前端和后端用 Python 编写的 HTML CSS 和引导程序来学习和开发 Flask 中的 Web 服务器网关界面。最后，我们将通过将机器学习模型集成到 Flask App 中，在 Face Recognition 项目上创建项目。

Genre: eLearning | MP4 | Video: h264, 1280×720 | 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