Face Recognition with Machine Learning + Deploy Flask App
What you'll 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
Requirements
- Should be at-least beginner level in Python
- Be able to understand HTML and CSS
- Basic Understanding of Machine Learning Concepts
Description
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.
Who this course is for:
- 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
Instructors
- 4.5 Instructor Rating
- 2,628 Reviews
- 92,017 Students
- 18 Courses
Hi,
We're team of Machine Learning experts, AI developers working together to advance the state of the art in artificial intelligence. You will be hearing from us when new courses are released, answering Q&A and many more.
We are here to help you stay on the cutting edge of Data Science and Technology.
Thanks,
datascience Anywhere Team
- 4.6 Instructor Rating
- 2,441 Reviews
- 73,047 Students
- 14 Courses
I am Sudheer working in Data Science with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Statistical algorithms. We mostly worked on Image Processing and Natural Language processing application. I also successfully deployed many data science-related projects in cloud platforms as a service in AWS, Google Cloud, etc.
- 4.5 Instructor Rating
- 2,525 Reviews
- 82,066 Students
- 16 Courses
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