What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence!

Teaching machines and let them take their own decisions with intelligence! That you are going to learn and apply in Machine Learning. So, we teach machines with data and while training on that data they creates their own algorithm on basis of which they will take decisions in future!

Okay! But how to teach a machine? There are three ways from which you can make machine learn:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

So, we trained through machines with data, lot's of data! So, every data has two aspects:

Labels
Features

We're going to discuss all of these in details in upcoming blogs!

Face detection in cameras, photo filters in Snapchat, Youtube recommendations, Spotify playlists or Instagram explore these all are possible due to ML!

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Face Detection and Recognition Model

Let's begin! The base model for Face Detection and Recognition. It uses face recognition library, which has face encodings to detect and recognize different faces. I've created a class where we can initialize it with the image path and label which save it's encoding then from predict function we check whether the encodings is same or not! If the encodings is same we return the image with label name. Else we return the image with default label Unknown .

Face Detection and Recognition API

Let's begin! Face Detection and Recognition API. I've used the same model as discussed in previous blog and used FastAPI framework to create the API and Uvicorn for the server to run it on Google Colab. The API has four request: Index (GET) : Homepage of API Upload (POST) : User can upload the image and label to be used as dataset. Target (POST) : Target image on which prediction has to be done. Prediction (GET) : Get the prediction. The purpose of this project was to understand the creation of a ML API and how to use it and it's use is restricted for educational purpose only.

Predicting whether the room is clean or messy from a Flutter based app.

It's an application made using Flutter, Dart and TensorflowLite which can be used to predict the cleanliness of the room. The app consist of an A.I. model which has been trained to classify the room as messy or clean. The model has been compressed and added to the app with TensorFlowLite and it works offline. The app allows user to pick a image (Room Image) by clicking on the gallery button and thus provide the image to model to classify as clean or messy. It can be downloaded by the following link for android devices. CleanVsMessy

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