Face Recognition with Machine Learning + Deploy Flask App

Face Recognition with Machine Learning + Deploy Flask App, available at $69.99, has an average rating of 4.45, with 115 lectures, based on 450 reviews, and has 24194 subscribers.

You will learn about Automatic Face Recognition in images and videos Automatically detect faces from images and videos Evaluate and Tune Machine Learning Building Machine Learning Model for Classification Make Pipeline Model for deploying your application Image Processing with OpenCV Data Preprocessing for Images Create REST APIs in Flask Template Inheritance in Flask Integrating Machine Learning Model in Flask App Deploy Flask App in Heroku Cloud This course is ideal for individuals who are Any one who want to learn image processing and build data science applications or Beginners on Python who want to data science project or Who want to start their career in artificial intelligence and data science or Data science beginner who want to build end to end data science project It is particularly useful for Any one who want to learn image processing and build data science applications or Beginners on Python who want to data science project or Who want to start their career in artificial intelligence and data science or Data science beginner who want to build end to end data science project.

Enroll now: Face Recognition with Machine Learning + Deploy Flask App

Summary

Title: Face Recognition with Machine Learning + Deploy Flask App

Price: $69.99

Average Rating: 4.45

Number of Lectures: 115

Number of Published Lectures: 115

Number of Curriculum Items: 115

Number of Published Curriculum Objects: 115

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will Learn

Automatic Face Recognition in images and videos
Automatically detect faces from images and videos
Evaluate and Tune Machine Learning
Building Machine Learning Model for Classification
Make Pipeline Model for deploying your application
Image Processing with OpenCV
Data Preprocessing for Images
Create REST APIs in Flask
Template Inheritance in Flask
Integrating Machine Learning Model in Flask App
Deploy Flask App in Heroku Cloud

Who Should Attend

Any one who want to learn image processing and build data science applications
Beginners on Python who want to data science project
Who want to start their career in artificial intelligence and data science
Data science beginner who want to build end to end data science project

Target Audiences

Any one who want to learn image processing and build data science applications
Beginners on Python who want to data science project
Who want to start their career in artificial intelligence and data science
Data science beginner who want to build end to end data science project

MLOPs: AI based Face Recognition Web App in Flask & Deploy

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?

Python
Image Processing with OpenCV
Image Data Preprocessing
Image Data Analysis
Eigenfaces with PCA
Face Recognition Classification Model with Support Vector Machines
Pipeline Model
Flask (Jinja Template, HTML, CSS, HTTP Methods)
Develop Face Recognition Web
Deploy Flask App in Cloud (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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Face Recognition Project Components

Lecture 3: Download all Resourses

Lecture 4: Install Python

Lecture 5: Clone Face Recognition Template

Lecture 6: Create and Install Virtual Environment & Packages

Lecture 7: Next step

Chapter 2: Image Processing with OpenCV

Lecture 1: OpenCV & Image

Lecture 2: What is Processing an Image (Information Extraction)

Lecture 3: Download Resources

Lecture 4: OpenCV: Values & Pixels

Lecture 5: OpenCV: Values & Pixels (Another Example)

Lecture 6: OpenCV: Read Image

Lecture 7: OpenCV: Pixels in Image

Lecture 8: OpenCV: Display Image

Lecture 9: OpenCV: Color Space

Lecture 10: OpenCV: Grayscale

Lecture 11: Image Resizing

Lecture 12: Face Detection

Lecture 13: Working on Videos

Chapter 3: Develop Face Recognition Model with Machine Learning from Scratch

Lecture 1: Face Recognition Model Introduction

Lecture 2: Download Resources

Lecture 3: About Data

Lecture 4: Face Recognition Training Flow

Lecture 5: Data Preprocess : Idea of Detect & Crop Face

Lecture 6: Data Preprocessing: Get Data

Lecture 7: Data Preprocessing: Import Required Libraries

Lecture 8: Data Preprocess: Get List of path of all Images

Lecture 9: Data Preprocess: Detect Face with Haar Cascade Classifier

Lecture 10: Data Preprocess: Crop Detected Face

Lecture 11: Data Preprocess: Crop All Faces

Lecture 12: Data Preprocess: Idea of Structuring Images

Lecture 13: Data Preprocessing: Structuring Data Part – 1

Lecture 14: Data Preprocessing: Exploratory Data Analysis

Lecture 15: Data Preprocessing: Filter Low Resolution Images and Resize images

Lecture 16: Data Preprocessing: Structure all Images

Lecture 17: Eigen Face: Flow

Lecture 18: Eigen: Mean Face and PCA

Lecture 19: Eigen Face: Get Optimal components for PCA

Lecture 20: Eigen Face: Save PCA ML model

Lecture 21: Eigen Face: Visualize Eigen Face

Lecture 22: Train Face Recognition Model Part – 1

Lecture 23: Train Face Recognition Model – Part 2

Lecture 24: Train Face Recognition Model Part – 3

Lecture 25: Best Estimator

Lecture 26: Model Evaluation

Lecture 27: Save Face Recognition Model

Lecture 28: Face Recognition Pipeline part 1

Lecture 29: Face Recognition Pipeline part 2

Lecture 30: Face Recognition Pipeline part 3

Lecture 31: Face Recognition Pipeline part 4

Lecture 32: Face Recognition Pipeline part 5

Lecture 33: Predictions part -1

Lecture 34: Predictions part -2

Lecture 35: Predictions part -3

Chapter 4: Face Recognition Project (Integrating HTML Model to Flask App)

Lecture 1: Download Resources

Lecture 2: Face Recognition Web App

Lecture 3: Install Visual Studio Code

Lecture 4: Folder Structure

Lecture 5: main.py (connect to virtual environment)

Lecture 6: main.py – basic app

Lecture 7: views.py and add url rule

Lecture 8: base.html part-1

Lecture 9: base.html part-2

Lecture 10: Home page

Lecture 11: App Page

Lecture 12: Gender App Page

Lecture 13: Gender App Page part 2

Lecture 14: Gender App Page part 3

Lecture 15: Gender App Page part 4

Lecture 16: Final App

Chapter 5: Deploy Web App in Heroku Cloud

Lecture 1: Getting ready for Deployment

Lecture 2: Install Git

Lecture 3: Setup Code for Deployment

Lecture 4: Push code to GitHub

Lecture 5: Deploy Flask App

Lecture 6: Heroku is not free tier anymore.

Lecture 7: You App Deployed in Heroku

Chapter 6: Deploying in another open-course cloud.

Lecture 1: Deploy Face Recognition Flask app in Railway.app (Free open source)

Lecture 2: Your Final App

Lecture 3: Download Final App

Chapter 7: Appendix – Python Crash Course

Lecture 1: Download the Resources

Lecture 2: Walk through on Jupyter Notebook

Lecture 3: Print Statements

Lecture 4: Escape and Insert keys

Lecture 5: Variables & Assignments

Lecture 6: Data Types

Lecture 7: Data Type Casting

Lecture 8: List

Lecture 9: List Methods

Lecture 10: Tuple

Lecture 11: Sets

Lecture 12: Dictionaries

Instructors

datascience Anywhere
Team of Engineers
G Sudheer
Instructor
Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 10 votes
2 stars: 8 votes
3 stars: 51 votes
4 stars: 156 votes
5 stars: 225 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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