To-doList.txt

Week 1: Basics of Machine Learning and Image Processing


Python Basics:
Learn fundamental Python programming concepts.
Resource: Python.org
Introduction to Machine Learning:

Understand the basics of supervised learning, classification, and evaluation metrics.
Resource: Coursera - Machine Learning by Andrew Ng
Introduction to Image Processing:

Learn about image manipulation, resizing, and basic filters.
Resource: OpenCV Tutorials


Week 2: Deep Learning and Face Recognition

Introduction to Neural Networks:
Understand neural network concepts and training.
Resource: Neural Networks and Deep Learning

Convolutional Neural Networks (CNNs):
Learn about CNNs and how they're used for image classification.
Resource: CS231n Convolutional Neural Networks
Face Detection and Recognition:

Explore OpenCV for face detection and recognition.
Resource: OpenCV Face Recognition


Week 3: Developing the Attendance System

Feature Extraction:
Extract facial features using pre-trained models like dlib.
Resource: dlib Facial Recognition
Training a Simple Classifier:

Learn how to train a basic classifier using extracted features.
Resource: Scikit-Learn Tutorials


Week 4: Deployment and Integration

Basic Web Development with Flask:
Create a basic web app using Flask to upload images.
Resource: Flask Official Documentation
Heroku Deployment:

Deploy your Flask web app using Heroku.
Resource: Heroku Python Support

#clarity
So, here's the deal - we're diving into the world of OpenCV and making cool face detection models. Imagine this: you'll be able to spot faces
like a pro! We're talking about learning stuff like YOLO and RCNN – they're like the secret sauce for detecting faces in a crowd.

Once we've got that going, picture this: a magical bounding box around every face in a photo. It's like giving each person a cool frame!

But wait, there's more. To take this up a notch, we can even display their names. How, you ask? Well, we'll stash a bunch of images in a 
database. Each image is like a VIP pass for a person, and we'll pair it up with their name. We can use Firebase or something similar – 
the tech wizards have got us covered.

And the grand finale? We're going to match the faces our model finds with the images in the database. It's like finding a twin, but in photos. 
We'll calculate this thing called "cosine similarity" – fancy, right? The one with the highest score gets its name displayed on that snazzy 
bounding box.