Create an Face Recognition (AI) project from scratch with Python, OpenCV , Machine Learning Algorithms and Flask
What you will learn from this Course:
- 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
Requirements for this Course:
- Should be at-least beginner level in Python
- Be able to understand HTML and CSS
- Basic Understanding of Machine Learning Concepts
Face Recognition Web Project utilizing Machine Learning in Flask Python
Face acknowledgement is perhaps the most generally utilized in my application. In the event that at all you need to create and send the application on the web just information on AI or profound learning isn’t sufficient. You additionally need to know the production of pipeline design and call it from the customer side, HTTP demand, and some more. At the same time, you may confront many difficulties while fostering the application. This course is organized so that you can be ready to foster the face acknowledgement based web application without any preparation.
What you will realize?
- Picture Processing with OpenCV
- Picture Data Preprocessing
- Picture Data Analysis
- Eigenfaces with PCA
- Face Recognition Classification Model with Support Vector Machines
- Pipeline Model
- Flagon (Jinja Template, HTML, CSS, HTTP Methods)
- At last, Face acknowledgement Web App
You will learn picture handling procedures in OpenCV and the ideas driving the pictures. We will likewise do the essential picture investigation and required preprocessing ventures for pictures.
For the preprocess pictures, we will separate components from the pictures, ie. registering Eigen pictures utilizing head part examination. With Eigen pictures, we will prepare the Machine learning demonstration and furthermore figure out how to test our model prior to sending, to get the best outcomes from the model we will tune with the Grid scan strategy for the best hyperparameters.
When our AI model is prepared, will we learn and foster a web worker entryway interphase in the carafe by delivering HTML CSS and bootstrap in the frontend and in the backend written in Python? At long last, we will make the task on the Face Recognition project by coordinating the AI 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
- Image Processing with OpenCV
- Develop Face Recognition Model with Machine Learning From Scratch
- Flask App
- Face Recognition Project (Integrating HTML Model to Flask APP)
- Appendix – Python Crash Course
- Extra Tips
- Bonus Lecture
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