This particular ML project is usually referred to as the "Hello World" of Machine Learning. The iris flowers dataset contains numeric attributes, and it is perfect for beginners to learn about supervised ML algorithms, mainly how to load and handle data. Also, since this is a small dataset, it can easily fit in memory without requiring special transformations capabilities or scaling
We need to read the image in RBG format and then convert it to a grayscale image. This will turn an image into a classic black and white photo. Then the next thing to do is invert the grayscale image also called negative image, this will be our inverted grayscale image. Inversion can be used to enhance details. Then we can finally create the pencil sketch by mixing the grayscale image with the inverted blurry image. This can be done by dividing the grayscale image by the inverted blurry image. Since images are just arrays, we can easily do this programmatically using the divide function from the cv2 library in Python.
Using TensorFlow and Keras library train a RNN, to predict the next word.
It is the Neural Network Machine Learning Project MNSIT Handwritten Digit Classification Challenge and using TensorFlow and CNN. It has very user friendly interface which is ideal for beginners.
Mathematical equation solver using character and symbol recognition using image processing and CNN.
Music recommender systems can suggest songs to users based on their listening patterns.
Stock Market Prediction And Forecasting Using Stacked LSTM
Prediction using Decision Tree Algorithm
As a security/defense analyst, try to find out the hot zone of terrorism.
ML Facial recognition to detect mood and suggest songs accordingly