Text Classification
Text classification involves assigning predefined categories or labels to text documents. In this subheading, students explore techniques such as bag-of-words, n-grams, and word embeddings for representing text data. They learn about popular classification algorithms like Naive Bayes, support vector machines, and deep learning models. Students gain practical experience in building text classification models for tasks such as spam detection, topic classification, and sentiment categorization.
Computer Vision
Computer vision is the branch of AI that deals with enabling computers to understand and interpret visual information. In the Computer Vision session, students explore the algorithms and techniques used to analyze and process images and videos. They gain insights into image recognition, object detection, image segmentation, and image generation.
Image Recognition
In this subheading, students dive into image recognition techniques that enable computers to identify and classify objects within images. They explore popular approaches like convolutional neural networks (CNNs) and transfer learning. Students learn about pre-trained models and how to fine-tune them for specific recognition tasks. They gain practical experience in building image recognition models and applying them to tasks such as object recognition, facial recognition, and scene understanding.
