Inspired by the ability of machines to quickly adapt and learn to changes of new tasks. The accuracy of the biometric face recognition task was designed by optimizing a few parameters like principal component analysis, number of hidden neurons and a few others.While doing this project, I realised that optimizers play an important role in the learning process of deep convolutional neural networks (CNN) model (Bera and Shrivastava, 2020).
This chatbot integrated with OpenAI ChatGPT API using Python, is a conversational agent that can interact with users in natural language. It uses the power of machine learning and natural language processing to understand user inputs and generate appropriate responses. It can provide users with a personalized and natural conversational experience.
Case Study: Training Faces of different looks to determine their mood.
Case Study: Graphical analysis of Airbnb locations in NYC.
Case Study: Diabetes Detection with Artificial Neural Network
Predicting California House prices is a project that aims to understand the factors that influence the prices of houses in California and use this
Case Study: Using Machine learning to check the prediction accuracy of a Cat vs Dog prediction.