Learn Data Scinece

  A Transfer Learning-based approach for detecting COVID-19  with Chest Radiography Images Environment setup/Project Requirements : Python Tensorflow, Matplotlib, Scikit-Learn libraries need to be installed. Jupyter Notebook Environment  Note : You can implement this project in Google-Colaboratory. I don't recommend Atom/Pycharm environment because it would be hard to visualize the data. Workflow: Importing Data (training images) Dividing the data set paths Append the images and labels into two empty lists Convert images into RGB format Apply LabelBinarizer Dividing training and testing data Loading the transfer learning model(VGG-16) Visualizing the sample data   Compiling and training the model. Visualizing the model predictions  Confusion matrix Testing accuracy. Step 1: Importing Data   Depending on the workflow of the project, we need to import the required libraries and the data required for the project. dataset = "...........input/Data" Step 2: Dividing the data

Computer vision-based Attendance System. Environment setup/Project Requirements : Python OpenCV, Numpy, face_recognition libraries need to be installed. Jupyter Notebook/Pycharm/ Atom  Note : You cannot implement this project in Google-Colaboratory or in any python online Interpreter Workflow: Importing Data (training images) Finding the faces and encodings  Comparing the encodings with the other face encodings Finding distances of the encodings. Visualizing the Findings  Finding the path to images automatically Looping through images Finding Encodings of the Face  Accessing WebCam Iterating through the faces and encodings Bounding boxes and visualizing the results  Attendance in CSV file Step 1: Importing data.  Firstly, we need to import the data in order to test the model. Since we have to test the model, whether it's predicting correctly or not, we need to take two inputs test image and the target image. imgElon = face_recognition.load_image_file( 'Images/ElonMusk.jpg'