Machine Learning

 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 .

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

Let's begin! The feature is Engine Size and the label is Co2 Emission.   On a linear equation, we can state that: y = mx + c Co2 Emission = (Slope * Engine Size) + Intercept As mentioned, Coefficient and Intercept in the simple linear regression, are the parameters of the fit line. Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. Notice that all of the data must be available to traverse and calculate the parameter. You can also try and run it by yourself!

Let's begin!  So, the project below will check whether the person wearing the mask correctly or not! Try out yourself by clicking on start!   Start So this models have two labels  You are wearing the mask properly which goes by Mask Either you are not wearing the mask properly or not wearing the mask at all! Which goes by No Mask The model is trained with 2000+ images which were uploaded with every possible scenario to check whether a person is wearing a mask correctly or not wearing mask at all! It was developed due to rising cases of COVID cases, it has been made to create awareness and encourage people to wear mask in a fun way! Happy Programming!

 Let's begin! COVID-19 has really affected our life! Even the President of U.S.A. and the first lady has been tested as positive. So, our first model will be dedicated on the prediction on the chances of whom are more likely to fall for the virus!

 So, how to make machines learn?